Short Answer:
Computer Vision:
Input: Images
Output: Knowledge of the scene (recognize objects, people, activity happening there, distance of the object from camera and each other, ...)
Methods: Image processing, machine learning, ...
Image Processing:
Input: Images
Output: Images (Might be in different formats, for example compressed images). No knowledge of the scene is given.
Methods: Different filtering, FFT, ...
Longer Answer:
Image processing is mainly focused on processing raw images and preparing them for other tasks such as in Computer Vision. Understanding the meaning of those images is a
Short Answer:
Computer Vision:
Input: Images
Output: Knowledge of the scene (recognize objects, people, activity happening there, distance of the object from camera and each other, ...)
Methods: Image processing, machine learning, ...
Image Processing:
Input: Images
Output: Images (Might be in different formats, for example compressed images). No knowledge of the scene is given.
Methods: Different filtering, FFT, ...
Longer Answer:
Image processing is mainly focused on processing raw images and preparing them for other tasks such as in Computer Vision. Understanding the meaning of those images is a Computer Vision task.
Computer Vision tries to do what a human brain does with the retinal input, it includes understanding and predicting the visual input. That could consist of segmentation, recognition, reconstruction (3D) and prediction (over video data). These give us the overall scene understanding.
Classically, many Computer Vision algorithms employed image processing and machine learning or sometimes other methods (e.g Variational Methods, Combinatorial approaches,...) to do the mentioned tasks. For example they used Image Processing techniques such as Edge Detection (e.g. Sobel Filter) to create Image Descriptors (e.g. SIFT) and then fed them to a Machine Learning algorithm to classify (for a recognition task).
Recently Convolutional Neural Networks (CNNs) do this purely through end-to-end machine learning. So you can just feed in the input images their labels and the filters are learned from the training data.
Image Processing and Computer Vision are related fields, but they serve different purposes when dealing with visual data, such as images or videos.
Image Processing
Image processing involves manipulating or enhancing images to extract useful information or to improve their quality. The primary goal is to process the image to make it more suitable for a specific task. This can involve operations such as:
- Filtering: Removing noise or enhancing specific features of an image (e.g., sharpening or blurring).
- Transformation: Resizing, rotating, or cropping images.
- Segmentation: Dividing an image into mean
Image Processing and Computer Vision are related fields, but they serve different purposes when dealing with visual data, such as images or videos.
Image Processing
Image processing involves manipulating or enhancing images to extract useful information or to improve their quality. The primary goal is to process the image to make it more suitable for a specific task. This can involve operations such as:
- Filtering: Removing noise or enhancing specific features of an image (e.g., sharpening or blurring).
- Transformation: Resizing, rotating, or cropping images.
- Segmentation: Dividing an image into meaningful regions (e.g., identifying objects in an image).
- Color adjustment: Altering the brightness, contrast, or color balance.
The main goal of image processing is to improve the image or prepare it for analysis.
Computer Vision
Computer vision goes a step further. It is about enabling machines to interpret, understand, and make decisions based on visual input. Computer vision often builds on the results of image processing but goes beyond just altering the image to making sense of it. Tasks in computer vision include:
- Object recognition: Identifying objects within an image, such as faces, cars, or animals.
- Image classification: Categorizing an image into predefined classes (e.g., "cat" vs. "dog").
- Feature extraction: Detecting important features (e.g., edges, textures) for further analysis.
- Motion analysis: Tracking the movement of objects in a video sequence.
While image processing is typically focused on enhancing the image itself, computer vision aims to enable machines to understand the content of the image or video and make intelligent decisions based on that.
In summary:
- Image processing is about improving or transforming images.
- Computer vision is about understanding what the image represents and making decisions based on that understanding.
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Image processing and computer vision are closely related fields, but they focus on different aspects of working with images. Here’s a breakdown of their differences:
Image Processing
- Definition: Image processing involves manipulating and enhancing images to improve their quality or to extract useful information. This can include techniques for filtering, noise reduction, image enhancement, and transformation.
- Goal: The primary goal is to prepare images for further analysis or to improve their visual appearance.
- Techniques: Common techniques include:
- Filtering (e.g., Gaussian, median)
- Image restorati
Image processing and computer vision are closely related fields, but they focus on different aspects of working with images. Here’s a breakdown of their differences:
Image Processing
- Definition: Image processing involves manipulating and enhancing images to improve their quality or to extract useful information. This can include techniques for filtering, noise reduction, image enhancement, and transformation.
- Goal: The primary goal is to prepare images for further analysis or to improve their visual appearance.
- Techniques: Common techniques include:
- Filtering (e.g., Gaussian, median)
- Image restoration
- Compression
- Color adjustment
- Edge detection
Computer Vision
- Definition: Computer vision is a broader field that focuses on enabling computers to interpret and understand visual information from the world. It encompasses the techniques and algorithms that allow machines to analyze and make decisions based on images or video.
- Goal: The goal is to extract meaningful information from images or sequences of images, enabling tasks such as object detection, recognition, tracking, and scene understanding.
- Techniques: Common techniques include:
- Object detection and recognition (e.g., using deep learning)
- Image segmentation
- Motion analysis
- 3D reconstruction
- Feature extraction
Summary
- Scope: Image processing is a subset of computer vision. While image processing focuses on the manipulation of images, computer vision aims to interpret and understand the content of those images.
- Applications: Image processing is often used in photography, medical imaging, and remote sensing, while computer vision is applied in robotics, autonomous vehicles, facial recognition, and augmented reality.
In essence, image processing deals with the "how" of modifying images, whereas computer vision addresses the "what" of understanding the content within those images.
Image processing is the step by step transformation of our input image into an output image. In between we might extract some information from the image to assist in the transformation. Basic image processing includes rotation, colour scale changes, crop, filter effects etc.
Computer vision on the other hands is the process to study an image or a group of images, use image processing and machine learning techniques, to mine information from image other than its properties. E.g. for computer vision can be detecting number of windows in the image of a building. Thing to note here is that output o
Image processing is the step by step transformation of our input image into an output image. In between we might extract some information from the image to assist in the transformation. Basic image processing includes rotation, colour scale changes, crop, filter effects etc.
Computer vision on the other hands is the process to study an image or a group of images, use image processing and machine learning techniques, to mine information from image other than its properties. E.g. for computer vision can be detecting number of windows in the image of a building. Thing to note here is that output of a computer vision technique is not just a transformed image but much more. Just like our brain, when views something stores information, like features of somebody’s face, similarly computer vision can also be used to extract such information from images (or videos). Computer vision algorithm can then be taught to decipher meaning from images from existing information it was fed earlier. Like our brain can recognise somebody by their face, similarly computer vision algorithms can be taught to recognise patterns, distinguish between objects etc.
So to sum it up, image processing takes in an input image and outputs an image after some defined transformations. Whereas computer vision takes an input image and outputs desired information which the algorithm was trained to
Image Processing:
- Definition: Image processing is the manipulation of digital images using various techniques to improve their quality, extract information, or alter their appearance.
- Operations: It involves a range of operations, such as filtering, noise reduction, contrast adjustment, and feature extraction.
- Image Enhancement: Image processing aims to enhance the visual quality of images, making them more suitable for human interpretation.
- Applications: Common applications include image resizing, color correction, and retouching in photography, as well as medical image enhancement and satellite
Image Processing:
- Definition: Image processing is the manipulation of digital images using various techniques to improve their quality, extract information, or alter their appearance.
- Operations: It involves a range of operations, such as filtering, noise reduction, contrast adjustment, and feature extraction.
- Image Enhancement: Image processing aims to enhance the visual quality of images, making them more suitable for human interpretation.
- Applications: Common applications include image resizing, color correction, and retouching in photography, as well as medical image enhancement and satellite image analysis.
- Examples: Applying filters to make a photo look sharper, adjusting brightness and contrast, and removing red-eye in photographs are common image processing tasks.
Computer Vision:
- Definition: Computer vision is a broader field that focuses on enabling computers to interpret and understand visual information from the world, similar to human vision.
- Interpretation: It goes beyond image manipulation and aims to understand and interpret visual data, such as images and videos.
- Tasks: Computer vision tasks include object detection, image recognition, tracking, 3D scene analysis, and even making decisions based on visual input.
- Applications: It finds applications in various domains, such as autonomous vehicles, robotics, facial recognition, medical image analysis, and augmented reality.
- Examples: Computer vision is used in self-driving cars to detect pedestrians, in surveillance systems to recognize individuals, and in healthcare for identifying anomalies in medical images, like X-rays and MRIs.
The line between Image Processing and Computer Vision is extremely blurry
In my opinion, Computer Vision is a subset of Image Processing.
Image Processing is commonly (and incorrectly) limited to image restoration, noise removal, image registration etc. This is an understandable confusion and I would like to offer my 2 cents.
Image Processing:
- Get data, whatever be the source. (1D - audio, 2D/color - image, 2D/color + time - Video)
- Understand that audio, images and videos are just mathematical numbers.
- Perform the required process, transform the numbers and extract the relevant data.
- Understand the p
The line between Image Processing and Computer Vision is extremely blurry
In my opinion, Computer Vision is a subset of Image Processing.
Image Processing is commonly (and incorrectly) limited to image restoration, noise removal, image registration etc. This is an understandable confusion and I would like to offer my 2 cents.
Image Processing:
- Get data, whatever be the source. (1D - audio, 2D/color - image, 2D/color + time - Video)
- Understand that audio, images and videos are just mathematical numbers.
- Perform the required process, transform the numbers and extract the relevant data.
- Understand the processed data and use it, display it, push it etc.
- If the data happens to be the second of the above mentioned types, then we term whatever it is that we do, as Image Processing.
Computer Vision:
Now assume the above source is to be treated as an Eye for a computer. Assume you are designing the visual system for HAL-9000
What are the things you would like HAL to be able to perform? Some of the common things include object tracking, face recognition and augmented reality.
Computer Vision uses basic Image Processing algorithms as a backbone, upon which further application are developed and then pushed forward as a product or service.
In my opinion, the output of "image processing" is images, while the output of "computer vision" is data with meaning. For example, if you start with a blurry picture of a cat, you might use image processing to make the image sharper, but you would use computer vision to identify it as a cat.
Computer vision attempts what biological vision attempts -- reading 2D images / videos of object surfaces, usually to identify or track single or multiple objects. It often employs AI techniques to do this, such as pattern recognition, machine learning, semantic ontologies, kalman filters, and may model object kinematics to predict their motion or behavior.
Image processing usually focuses more on lower level primitives than vision and works with a greater diversity of image types: 2D, 3D, multispectral, multimodal, or video. IP studies the math and DSP that underpin techniques like image enh
Computer vision attempts what biological vision attempts -- reading 2D images / videos of object surfaces, usually to identify or track single or multiple objects. It often employs AI techniques to do this, such as pattern recognition, machine learning, semantic ontologies, kalman filters, and may model object kinematics to predict their motion or behavior.
Image processing usually focuses more on lower level primitives than vision and works with a greater diversity of image types: 2D, 3D, multispectral, multimodal, or video. IP studies the math and DSP that underpin techniques like image enhancement, segmentation, registration, object shape modeling, multi-image stitching, panorama generation, and even image generation (3D tomographic reconstruction, 2D/3D projection, 3D graphics, game rendering engines, etc).
Generally students learn the subjects in this sequence: DSP then image processing then computer vision. The techniques of the latter build upon the former, but they intersect in many ways.
Image processing and computer vision are closely related fields that involve the manipulation and analysis of visual data. However, they have distinct goals and methodologies.
Image Processing
Image processing involves the manipulation of images to enhance their quality or extract useful information. It includes a variety of techniques to improve the appearance of images, restore damaged images, compress images for storage and transmission, and analyze image content.
Key Techniques:
- Enhancement: Improving image quality through adjustments in contrast, brightness, and noise reduction.
- Restoration: C
Image processing and computer vision are closely related fields that involve the manipulation and analysis of visual data. However, they have distinct goals and methodologies.
Image Processing
Image processing involves the manipulation of images to enhance their quality or extract useful information. It includes a variety of techniques to improve the appearance of images, restore damaged images, compress images for storage and transmission, and analyze image content.
Key Techniques:
- Enhancement: Improving image quality through adjustments in contrast, brightness, and noise reduction.
- Restoration: Correcting distortions or degradations in an image, such as blurriness or missing information.
- Segmentation: Dividing an image into meaningful regions or objects.
- Compression: Reducing the size of image files for efficient storage and transmission.
- Feature Extraction: Identifying and isolating specific elements within an image, such as edges, textures, or objects.
Computer Vision
Computer vision, on the other hand, focuses on enabling computers to interpret and understand visual information in a manner similar to human vision. It aims to develop algorithms and systems that can perform tasks such as object recognition, scene understanding, and activity recognition.
Key Techniques:
- Object Detection and Recognition: Identifying and classifying objects within an image or video.
- Image Classification: Assigning labels to an entire image based on its content.
- Image Segmentation: Partitioning an image into segments that correspond to different objects or regions.
- Motion Analysis: Understanding and interpreting motion in video sequences, such as tracking moving objects.
- 3D Reconstruction: Creating three-dimensional models from two-dimensional images.
Applications
- Image Processing Applications:Medical Imaging: Enhancing and analyzing medical images for diagnosis.Satellite Imaging: Processing satellite images for environmental monitoring and mapping.Photography: Improving photo quality through various enhancement techniques.
- Computer Vision Applications:Autonomous Vehicles: Enabling vehicles to navigate by recognizing and understanding their surroundings.Surveillance: Monitoring and analyzing video feeds for security purposes.Robotics: Allowing robots to interact with and understand their environment.
Overlap and Integration
While image processing and computer vision are distinct fields, they often overlap. For example, preprocessing an image through image processing techniques can improve the performance of computer vision algorithms. Both fields leverage similar mathematical and computational tools, such as machine learning, deep learning, and statistical methods, to achieve their goals.
In summary, image processing focuses on manipulating and enhancing images, while computer vision aims to understand and interpret visual information from the world. Together, they enable a wide range of applications that rely on visual data.
There isn't a proper defined boundry between the two , infact image processing is used extensively to achieve computer vision.
Unlike image processing, the scope of computer vision is not limited to images and for most of the cases video feeds from real world is used. The ultimate objective of computer vision is to achieve something which a human (mind + eye) can do easily like motion detection, object identification, movement trajectory, background substraction etc using image processing + machine learning and other related techniques. Also, in computer vision we establish correlation between
There isn't a proper defined boundry between the two , infact image processing is used extensively to achieve computer vision.
Unlike image processing, the scope of computer vision is not limited to images and for most of the cases video feeds from real world is used. The ultimate objective of computer vision is to achieve something which a human (mind + eye) can do easily like motion detection, object identification, movement trajectory, background substraction etc using image processing + machine learning and other related techniques. Also, in computer vision we establish correlation between subsequent frames of video for extracting valuable insights.
Image processing and computer vision are related but distinct fields within computer science that deal with the analysis and manipulation of images. While they share some common techniques and applications, their objectives and focuses are different:
- Image processing:
- Image processing is a set of techniques used to manipulate and transform digital images to improve their quality, extract useful information, or create visually appealing effects.
- The primary goal of image processing is to enhance the image or prepare it for further analysis by applying various algorithms and transformations.
- Image p
Image processing and computer vision are related but distinct fields within computer science that deal with the analysis and manipulation of images. While they share some common techniques and applications, their objectives and focuses are different:
- Image processing:
- Image processing is a set of techniques used to manipulate and transform digital images to improve their quality, extract useful information, or create visually appealing effects.
- The primary goal of image processing is to enhance the image or prepare it for further analysis by applying various algorithms and transformations.
- Image processing techniques include filtering, noise reduction, contrast enhancement, color correction, resizing, and edge detection, among others.
- Image processing typically operates at the pixel level, dealing with the individual elements that make up an image.
- Examples of image processing applications include removing red-eye from photos, adjusting the brightness and contrast of an image, or compressing images for efficient storage and transmission.
- Computer vision:
- Computer vision is a subfield of artificial intelligence that aims to enable computers to understand and interpret the content of digital images and videos, much like humans do.
- The primary goal of computer vision is to extract high-level, semantic information from images, such as object recognition, scene understanding, and activity analysis.
- Computer vision techniques involve a combination of image processing methods, machine learning algorithms, and pattern recognition to achieve tasks like object detection, facial recognition, and optical character recognition (OCR).
- Computer vision operates at a higher level of abstraction, focusing on the interpretation of the image content rather than the individual pixels.
- Examples of computer vision applications include autonomous vehicles, which use computer vision to navigate and avoid obstacles, and facial recognition systems for security or authentication purposes.
In summary, image processing is focused on manipulating and enhancing images, while computer vision aims to understand and interpret the content of images. Image processing techniques often serve as a foundation for computer vision applications, as preprocessed images can be more effectively analyzed and interpreted.
In general, image processing refers to enhancing certain features of an image whereas computer vision is aimed at understanding the features so enhanced.
An easy real world example would be your phone camera. When you take an image, you can modify various parameters and apply filters to make your image look good. This achieved through image processing algorithms. Whereas if you use the same camera to pick faces while taking a picture or decoding barcodes, it is a subset of what computer vision entails. The line between the two though is very thin and seldom non existent. A computer vision engin
In general, image processing refers to enhancing certain features of an image whereas computer vision is aimed at understanding the features so enhanced.
An easy real world example would be your phone camera. When you take an image, you can modify various parameters and apply filters to make your image look good. This achieved through image processing algorithms. Whereas if you use the same camera to pick faces while taking a picture or decoding barcodes, it is a subset of what computer vision entails. The line between the two though is very thin and seldom non existent. A computer vision engineer or researcher is required to be proficient in image processing as generally computer vision algorithms build on image processing ones.
If you are thinking of working/researching in Computer Vision, you should be sure to take an advanced image processing class.
Thanks for the A2A, Suraj.
Although the subjects of image processing and computer vision are closely related, their scopes and goals are different.
Image processing studies methods and techniques for modifying and analyzing specific images. It includes procedures like mathematical enhancements, image enhancement, noise reduction, image restoration, and compression of images. Improving the visual appearance of images or recovering certain information from them is the main objective of image processing. Image Processing: Image processing techniques find applications in various domains, including photography, medical imagin
Although the subjects of image processing and computer vision are closely related, their scopes and goals are different.
Image processing studies methods and techniques for modifying and analyzing specific images. It includes procedures like mathematical enhancements, image enhancement, noise reduction, image restoration, and compression of images. Improving the visual appearance of images or recovering certain information from them is the main objective of image processing. Image Processing: Image processing techniques find applications in various domains, including photography, medical imaging, satellite imaging, surveillance, and digital forensics
Computer vision is the study of interpreting and comprehending visual data, such as images and videos. It covers a broader range of operations, including 3D reconstruction, image interpretation, recognizing objects, tracking, and object recognition. Computer vision aims to replicate human visual perception and enable machines to extract meaningful information from visual inputs. Computer Vision: Computer vision techniques are applied in diverse fields, such as autonomous vehicles, robotics, augmented reality, facial recognition, gesture recognition, human-computer interaction, and industrial automation. The focus is on understanding and extracting high-level information from visual data for real-world applications.
In my view, computer vision makes use of many image processing techniques, whereas image processing is a collection of different signal processing techniques. The former is more specific and always aligned with some missions or specific industrial tasks like the detection of a particular object, while the latter one is more general and looks more introductory.
(Refer to Ravindra Bagale)
Image processing
- title says that it processes image, means does some transformations on an image. That means it does some smoothing, sharpening, contrasting, stretching.. on the image to enhance image more rea
In my view, computer vision makes use of many image processing techniques, whereas image processing is a collection of different signal processing techniques. The former is more specific and always aligned with some missions or specific industrial tasks like the detection of a particular object, while the latter one is more general and looks more introductory.
(Refer to Ravindra Bagale)
Image processing
- title says that it processes image, means does some transformations on an image. That means it does some smoothing, sharpening, contrasting, stretching.. on the image to enhance image more readable. Both input and output of the processing are images.
Computer vision
- the ultimate goal is to use computers to emulate human vision, including learning and being able to make inferences and take actions based on visual inputs.
Computer VIsion helps gain high-level understanding through the interpretation of visual data. The interpretation of visual data is used for automating tasks, increasing efficiency and accuracy which are not possible with a human pair of eyes.
Example: Detection of defects in products moving on a high-speed factory belt.
Computer Vision uses many Algorithms and processes to derive inputs from visual data. One such technique is Image Processing.
Image Processing is a technique used to enhance the quality of images by tuning its parameters and features, like removing distortions, sharpening, smooth
Computer VIsion helps gain high-level understanding through the interpretation of visual data. The interpretation of visual data is used for automating tasks, increasing efficiency and accuracy which are not possible with a human pair of eyes.
Example: Detection of defects in products moving on a high-speed factory belt.
Computer Vision uses many Algorithms and processes to derive inputs from visual data. One such technique is Image Processing.
Image Processing is a technique used to enhance the quality of images by tuning its parameters and features, like removing distortions, sharpening, smoothing, stretching etc. Image processing is a subset of Computer Vision and is used as prerequisite step before Deep Learning Model of Computer VIsion can start extracting data from the visuals
Image Processing applications include - Rescaling image, Correcting illumination, Changing tones etc.
Computer Vision applications include- Object detection, Face detection, Handwriting recognition etc.
The basic difference between Computer Vision vs Image processing is described via this image.
Image Processing — Input = Image, Ouput = Image
Computer Vision — Input = Image, Ouput = Information
In general, for a given input image, we can classify Image Processing and Computer Vision as follows:
- Image Processing ( output = Image ). e.g. Edge Detection, Gaussian Filtering, etc.
- Computer Vision ( output = Information ). e.g. Face Recognition, Face Detection, etc.
Computer Vision is also called Machine Vision and has many important problem domains:
- Image Classification / Recognition
- Object Detection
- Face
The basic difference between Computer Vision vs Image processing is described via this image.
Image Processing — Input = Image, Ouput = Image
Computer Vision — Input = Image, Ouput = Information
In general, for a given input image, we can classify Image Processing and Computer Vision as follows:
- Image Processing ( output = Image ). e.g. Edge Detection, Gaussian Filtering, etc.
- Computer Vision ( output = Information ). e.g. Face Recognition, Face Detection, etc.
Computer Vision is also called Machine Vision and has many important problem domains:
- Image Classification / Recognition
- Object Detection
- Face Recognition
- Semantic Segmentation
- Instance Segmentation
- Keypoint Detection
- Activity Recognition
- many more. . .
Image processing, computer vision and pattern recognition are all intertwined. Computer vision isn't too much different from image processing. Computer vision deals with certain aspects of image acquisition too, which usually isn't studied in image processing.
Pattern recognition is a tool that can be applied to solve various problems of image processing and computer vision. If you're going to get a masters in computer vision, taking pattern recognition course would be mandatory.
You can look at startup companies working in this area. There are a few big companies too, like Microsoft, Google, Xe
Image processing, computer vision and pattern recognition are all intertwined. Computer vision isn't too much different from image processing. Computer vision deals with certain aspects of image acquisition too, which usually isn't studied in image processing.
Pattern recognition is a tool that can be applied to solve various problems of image processing and computer vision. If you're going to get a masters in computer vision, taking pattern recognition course would be mandatory.
You can look at startup companies working in this area. There are a few big companies too, like Microsoft, Google, Xerox etc.
Image Processing as title suggests processes image, means does some transformations on image. that means may be it does some smoothing,sharpening, contrasting,stretching.. on the image for making image more readable that is input and output of a process are images.
Computer Vision is to use computers to mimic human vision, that includes image classification, object detection, etc.
Image processing and computer vision are two branches of computer science and engineering that focus on the analysis, modification, and interpretation of visual information from the outside environment, generally in the form of pictures or video.
Image Processing: Image processing is the modification or enhancement of digital pictures using various techniques and algorithms. These approaches are used to extract relevant information from photos, improve image quality, and conduct other image operations. Some frequent image processing jobs include:
- Image Enhancement: Adjusting contrast, brightness
Image processing and computer vision are two branches of computer science and engineering that focus on the analysis, modification, and interpretation of visual information from the outside environment, generally in the form of pictures or video.
Image Processing: Image processing is the modification or enhancement of digital pictures using various techniques and algorithms. These approaches are used to extract relevant information from photos, improve image quality, and conduct other image operations. Some frequent image processing jobs include:
- Image Enhancement: Adjusting contrast, brightness, and sharpness to improve the visual quality of an image.
- Image Filtering: Applying filters to remove noise or highlight specific features in an image.
- Image Segmentation: Dividing an image into meaningful regions or objects.
- Image compression is the process of shrinking a picture while preserving acceptable quality.
- Image restoration is the process of removing artifacts or flaws from pictures.
Computer vision goes beyond simple image processing to enable computers to grasp and interpret visual input in the same way that people do. It tries to offer machines the ability to sense and analyze their surroundings using visual data. Among the most important tasks in computer vision are:
- Object detection is the process of identifying and finding objects in an image or video stream. Recognizing and classifying items in photos or videos is known as object recognition.
- Image Understanding: The extraction of high-level information from pictures, such as scene comprehension or context.
- Pose estimation is the process of calculating the 3D location and orientation of objects or humans in photographs or movies.
- Tracking is the process of following the movement of objects or people in video sequences through time.
- Gesture Recognition is the process of recognizing and understanding gestures produced by people or other things.
Machine learning and deep learning techniques, such as convolutional neural networks (CNNs), are often used in computer vision to train models on big datasets for diverse visual tasks. These models may be utilized for a variety of applications, including autonomous cars and robotics, as well as facial identification and medical picture analysis.
Here is an intuitive answer:
In Computer vision :
Input (Image) + Transformation/Operations (Information extraction/Image Processing operations) = Output (Information from the image)
In Image processing :
Input (Image) + Transformations(Eg.Convolution/Sharpening etc)
= Output (Image)
Image Processing can be used by Computer Vision while understanding information from an image.
I had written a post explaining difference between Computer Vision, Image processing and Computer Graphics:
Distinguishing between Image Processing and Computer Vision and Computer Graphics
Image processing in computer vision involves the manipulation of images to extract meaningful information or enhance certain features. It's a crucial aspect of computer vision, which is a field of study focused on enabling computers to interpret and understand visual information from the world, similar to how humans perceive and interpret images.
Image processing enables a computer system to analyze and understand the visual information in an image, leading to the identification of a specific object. Similar principles are applied in various computer vision applications, ranging from medical im
Image processing in computer vision involves the manipulation of images to extract meaningful information or enhance certain features. It's a crucial aspect of computer vision, which is a field of study focused on enabling computers to interpret and understand visual information from the world, similar to how humans perceive and interpret images.
Image processing enables a computer system to analyze and understand the visual information in an image, leading to the identification of a specific object. Similar principles are applied in various computer vision applications, ranging from medical image analysis to object detection in autonomous vehicles.
Computer vision and image processing are distinct yet interconnected fields. Image processing involves the manipulation of pixel data in images to enhance them or extract valuable information. Techniques such as filtering, image transformation, and noise reduction fall under image processing.
On the other hand, computer vision goes a step further by aiming to interpret and understand the content of those images.
While image processing focuses on improving image quality or extracting specific features, computer vision seeks to enable machines to recognize objects, understand scenes, and make deci
Computer vision and image processing are distinct yet interconnected fields. Image processing involves the manipulation of pixel data in images to enhance them or extract valuable information. Techniques such as filtering, image transformation, and noise reduction fall under image processing.
On the other hand, computer vision goes a step further by aiming to interpret and understand the content of those images.
While image processing focuses on improving image quality or extracting specific features, computer vision seeks to enable machines to recognize objects, understand scenes, and make decisions based on visual inputs, akin to human visual perception.
There are some confusion, how people use these terms. Often, they refer to the same activities. And even there are multiple definitions on them.
Actually in general, image processing is part of computer vision, but it can be also beyond it. Image processing deals with the image itself, modifying it to for different purposes. This can be some preparation for extracting information (part of computer vision), but also to enhance to look better. This latter case is not computer vision.
In contrast, computer vision means the whole process to extract information from images, videos or even some specia
There are some confusion, how people use these terms. Often, they refer to the same activities. And even there are multiple definitions on them.
Actually in general, image processing is part of computer vision, but it can be also beyond it. Image processing deals with the image itself, modifying it to for different purposes. This can be some preparation for extracting information (part of computer vision), but also to enhance to look better. This latter case is not computer vision.
In contrast, computer vision means the whole process to extract information from images, videos or even some special optical devices. To reach the goals, it mostly involves image processing. techniques, too.
Image processing is focused around more image level manipulations such as image enhancements such as blurring, compression, convex boundaries, and image quality assessment. Computer vision is about features, representation, detection, segmentation, tracking and associating multiple domains. So image processing is a prerequisite to understand traditional computer vision.
At first place we can say that Image processing is a part of Computer vision (CV).
Image processing primarily involves processing (such as feature extraction, denoising, transforming, segmentation, etc.) on 2-D image.
Whereas CV involves (in addition to image processing) the processing on sequence of images (video), or multiple images so as to get a deeper knowledge insight of an image or video. e.g. Image depth analysis (in which approx. objects' distance from camera is computed) is a part of computer.
If you see any type of advances currently happening into human computer interactions (visual),
At first place we can say that Image processing is a part of Computer vision (CV).
Image processing primarily involves processing (such as feature extraction, denoising, transforming, segmentation, etc.) on 2-D image.
Whereas CV involves (in addition to image processing) the processing on sequence of images (video), or multiple images so as to get a deeper knowledge insight of an image or video. e.g. Image depth analysis (in which approx. objects' distance from camera is computed) is a part of computer.
If you see any type of advances currently happening into human computer interactions (visual), it's all is Computer Vision.
Image processing is a kind of computer vision. Image processing means converting an image into a digital form and performing certain operations on it. As a result, it is possible to extract some information from such an image.
But computer vision is a wide area in which deep learning is used to perform tasks such as image processing, image classification, object detection, object segmentation, image coloring, image reconstruction, and image synthesis.
Read more here:
Image processing produces images, usually for human consumption, while computer vision aims to produce high level semantic descriptions of images.
Computer vision is still an active area of research. It is questionable if image processing is still an active area of research because of all the advances in hardware, network bandwidth, etc. Currently, it seems like both are used interchangeably. Though, in the medical imaging field, image processing is big because of acquisition methods/protocols
The primary difference between computer vision and image processing lies in their objectives and scope. Image processing is primarily concerned with the preprocessing and enhancement of images, making them suitable for further analysis. It involves techniques like segmentation, edge detection, and morphological operations.
Computer vision, however, aims to enable machines to understand and interpret visual information. It leverages image processing as a foundational tool but extends beyond it by incorporating algorithms for object detection, scene understanding, and pattern recognition.
In essen
The primary difference between computer vision and image processing lies in their objectives and scope. Image processing is primarily concerned with the preprocessing and enhancement of images, making them suitable for further analysis. It involves techniques like segmentation, edge detection, and morphological operations.
Computer vision, however, aims to enable machines to understand and interpret visual information. It leverages image processing as a foundational tool but extends beyond it by incorporating algorithms for object detection, scene understanding, and pattern recognition.
In essence, while image processing focuses on improving the image itself, computer vision focuses on extracting meaningful information and insights from the image.
Image processing
The output is another image which is better and used to remove undesired features on the image.
Computer vision
The output is data which simulates how human brain should do to that image.
Computer Graphics is about drawing things on the screen with pixels, using mathematics and physics (trigonometry, lighting, shading, curvature, etc) to give the impression of objects to a human viewer.
The output requirements can be simple eg arcade games, or complex eg realistic rendition for movies.
Image Processing is about taking a digital input (black&white photo or colour photo or scanned image or xerox copy etc) and using mathematics and physics (trigonometry, lighting, shading, curvature, etc) to extract details of objects in that input.
The output requirements can be simple eg finding l
Computer Graphics is about drawing things on the screen with pixels, using mathematics and physics (trigonometry, lighting, shading, curvature, etc) to give the impression of objects to a human viewer.
The output requirements can be simple eg arcade games, or complex eg realistic rendition for movies.
Image Processing is about taking a digital input (black&white photo or colour photo or scanned image or xerox copy etc) and using mathematics and physics (trigonometry, lighting, shading, curvature, etc) to extract details of objects in that input.
The output requirements can be simple eg finding lines or detecting colours (which can be for non-AI purposes) or complex eg finding faces or detecting emotions (which can be for AI purposes)
Computer Vision and Machine Learning are two core branches of Computer Science that can function, and power very sophisticated systems that rely on CV and ML algorithms exclusively but when you combine the two, you can achieve even more. That is to say, you don't need to learn one before the other or even simultaneously and there are specific use cases or domains where you would consider a marriag
Computer Vision and Machine Learning are two core branches of Computer Science that can function, and power very sophisticated systems that rely on CV and ML algorithms exclusively but when you combine the two, you can achieve even more. That is to say, you don't need to learn one before the other or even simultaneously and there are specific use cases or domains where you would consider a marriage between the two.
Here's an example: A project I worked on back in my engineering days was aimed at recognizing human hand gestures in real time to perform simple tasks: opening/closing apps, switching PowerPoint slides, playing/pausing music and so on. This project, as you can guess, needed two core ingredients to succeed:
1. The ability to make the computer see the gesture being performed.
2. The ability to understand the gesture being performed.
The 1st part is a CV problem. In my case, we used skin detection algorithms that would capture multiple frames while the gesture was being performed, detect the position within each hand by converting each image to gray-scale and omitting the background noise (thus focusing just on the hand) and tracing the path followed. Thus, here the Image Processing steps lead to Feature Extraction that will be used for further processing. All this was executed using Computer Vision techniques and this part of the code base basically enabled the computer to use a web camera to "see" what was going on and record it in a language that it understood.
(Preliminary image samples from the project)
The second phase was making the computer understand what was going on. More often than not, this is accompanied by an ability to "recall" or "recognize" a given pattern. In this example, the pattern was the path followed by the user (imagine a very linear path for straight up/down/left/right gestures while a curvy one for anything involving circular movements). Pattern matching was essential here and even before the given pattern could be matched to the computer's knowledge base, it's knowledge base had to be constructed. This is where Machine Learning comes. We used a popular ML technique using a Hidden Markov Model to adapt to our system needs. We trained it over hundreds of samples (basically, performed each gesture several times) so that ...
Computer Vision:
- Focus: Computer vision aims to enable computers to understand and interpret visual information from the world, much like human vision.
- Objectives: It is concerned with tasks such as object recognition, image classification, scene understanding, and 3D reconstruction.
- Processing: Computer vision often involves higher-level interpretation and understanding of images and videos.
- Applications: Used in autonomous vehicles, facial recognition, medical imaging, and robotics, among others.
Digital Image Processing:
- Focus: Digital image processing deals with the manipulation and enhancement
Computer Vision:
- Focus: Computer vision aims to enable computers to understand and interpret visual information from the world, much like human vision.
- Objectives: It is concerned with tasks such as object recognition, image classification, scene understanding, and 3D reconstruction.
- Processing: Computer vision often involves higher-level interpretation and understanding of images and videos.
- Applications: Used in autonomous vehicles, facial recognition, medical imaging, and robotics, among others.
Digital Image Processing:
- Focus: Digital image processing deals with the manipulation and enhancement of images.
- Objectives: It focuses on improving image quality, enhancing details, and extracting specific information from images.
- Processing: Involves techniques like noise reduction, image filtering, and enhancement to alter the visual aspects of images.
- Applications: Used in medical image enhancement, satellite image processing, and image compression, among others.
Multimedia Computing:
- Focus: Multimedia computing is a broader field encompassing the processing, transmission, and understanding of various forms of multimedia data, including text, audio, images, and video.
- Objectives: It aims to integrate different types of data into a unified system and provide methods for data representation and retrieval.
- Processing: Includes text processing, audio signal analysis, video processing, and the integration of multiple media types.
- Applications: Used in web content delivery, digital media retrieval, multimedia databases, and interactive multimedia systems.
Vision/Visual perception is basically the ability to interpret the surroundings by processing the information in the light.Now,in humans this is done by eyes and is called human vision.Similarly,computer vision is imparting vision to the computer,that is,giving it the abilities of human vision by electronically understanding images.Now,this process of imparting computer vision involves various steps like acquiring different kinds of images,processing them and extracting the info from them to make decision.The step of processing of images is nothing but digital image processing.Simply put, Digi
Vision/Visual perception is basically the ability to interpret the surroundings by processing the information in the light.Now,in humans this is done by eyes and is called human vision.Similarly,computer vision is imparting vision to the computer,that is,giving it the abilities of human vision by electronically understanding images.Now,this process of imparting computer vision involves various steps like acquiring different kinds of images,processing them and extracting the info from them to make decision.The step of processing of images is nothing but digital image processing.Simply put, Digital image processing is a step in imparting vision to computer.
Multimedia computing/computer is a computer which is optimized for processing multimedia information like audio,video etc.
Computer vision is related to image processing in the sense that the computer vision front-end is comprised of image processing techniques such as noise reduction, whitening or image enhancement. There is a lot of overlap between computer vision and image processing.
Machine learning on the other hand is flexible as it can be used in either computer vision or image processing.
Image processing
- The goal of image processing is to enhance or compress image/video information.
- Uses pixel-wise operations such as transforming one image into another. For example applying a rotation on pixels.
- There is no e
Computer vision is related to image processing in the sense that the computer vision front-end is comprised of image processing techniques such as noise reduction, whitening or image enhancement. There is a lot of overlap between computer vision and image processing.
Machine learning on the other hand is flexible as it can be used in either computer vision or image processing.
Image processing
- The goal of image processing is to enhance or compress image/video information.
- Uses pixel-wise operations such as transforming one image into another. For example applying a rotation on pixels.
- There is no extraction of meaningful information from those pixel-wise operations.
Computer vision
- The goal of computer vision is to extract meaningful information from images/videos. Such as whether a certain object is present or not in a particular scene.
- Computer vision is not limited to pixel-wise operations it can be complex, far more complex than image processing.
- Those complex operations can be summarized into feature detectors which can provide rich information about the contents of the image/video.
Machine learning
- The goal of machine learning is to optimize differentiable parameters so that a certain loss/cost function is minimized.
- Machine learning can be used in both image processing and computer vision but it has found more use in computer vision than in image processing.
- In ML the loss function can have a physical meaning in which case the features learnt can be quite informative but this is not necessarily the case for all situations.
The relationship between them can be quite complex. For example, convolutional neural networks are using all three techniques, convolutions are from image processing as they work on per small pixel neighborhood basis, the need to extract image content is from computer vision while the kernel parameters are adjusted using machine learning techniques.
Thus these fields overlap considerably but are different.
Hope this helps.
Human Vision:
- Biological: Human vision is a biological process carried out by the human eye and the brain.
- Innate Understanding: Humans naturally understand and interpret visual information, recognizing objects, faces, and scenes effortlessly.
- Adaptability: Human vision adapts to various lighting conditions, can perceive depth and motion, and is highly contextual and intuitive.
- Emotional Interpretation: Humans can interpret and emotionally respond to visual content, such as art, facial expressions, and body language.
Computer Vision:
- Artificial: Computer vision is a technology-driven field that uses
Human Vision:
- Biological: Human vision is a biological process carried out by the human eye and the brain.
- Innate Understanding: Humans naturally understand and interpret visual information, recognizing objects, faces, and scenes effortlessly.
- Adaptability: Human vision adapts to various lighting conditions, can perceive depth and motion, and is highly contextual and intuitive.
- Emotional Interpretation: Humans can interpret and emotionally respond to visual content, such as art, facial expressions, and body language.
Computer Vision:
- Artificial: Computer vision is a technology-driven field that uses software and hardware to process and understand visual data.
- Learned Understanding: Computers require training and algorithms to understand and interpret visual data. It doesn't have inherent common-sense understanding.
- Limited Adaptability: Computer vision may struggle with certain lighting conditions, require specialized sensors, and often lack the depth and motion perception that humans possess.
- Lack of Emotional Interpretation: Computer vision systems can't inherently understand or respond to emotions conveyed in images or videos. They rely on predefined rules and patterns.
Image Processing is a sub domain of Computer Vision. Image Processing involves in primitive operations such as Image Enhancement, on the other hand Computer Vision involves in cognitive operations such as automatic recognition of objects and events. For more detailed answer see my previous answer to the following question:
What is image processing? [ https://www.quora.com/What-is-image-processing-1
Image Processing is a sub domain of Computer Vision. Image Processing involves in primitive operations such as Image Enhancement, on the other hand Computer Vision involves in cognitive operations such as automatic recognition of objects and events. For more detailed answer see my previous answer to the following question:
What is image processing? [ https://www.quora.com/What-is-image-processing-1 ]
I will give you a simple example to understand the involvement of image processing and computer vision. Suppose you want to build a automated system that reads the text and extracts the meaning from it. To built the system you need an input device such as ...
Digital signal processing (DSP): DSP refers to various techniques for improving the accuracy and reliability of digital communications. The theory behind DSP is quite complex. Basically, DSP works by clarifying, or standardizing, the levels or states of a digital signal.
Digital Image Processing (DIP): Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. It is a type of signal processing in which input is an image and output may be image or characteristics/features associated with that image. Nowada
Digital signal processing (DSP): DSP refers to various techniques for improving the accuracy and reliability of digital communications. The theory behind DSP is quite complex. Basically, DSP works by clarifying, or standardizing, the levels or states of a digital signal.
Digital Image Processing (DIP): Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. It is a type of signal processing in which input is an image and output may be image or characteristics/features associated with that image. Nowadays, image processing is among rapidly growing technologies. It forms core research area within engineering and computer science disciplines too.
Roughly speaking, computer graphics traditionally referred to the process of creating images from abstract models. A computer game, for example, might internally keep track of Mario as a large list of points, where each point has three numbers representing its (x, y, z) coordinates. Then, given the coordinates of the camera, and the direction its facing, the computer will calculate the color at each row and column in the final image of Mario that you see on your screen.
Image processing refers to the process of starting with an existing image and refining it in some way to obtain another image.
Roughly speaking, computer graphics traditionally referred to the process of creating images from abstract models. A computer game, for example, might internally keep track of Mario as a large list of points, where each point has three numbers representing its (x, y, z) coordinates. Then, given the coordinates of the camera, and the direction its facing, the computer will calculate the color at each row and column in the final image of Mario that you see on your screen.
Image processing refers to the process of starting with an existing image and refining it in some way to obtain another image. For example, if you take a picture with your camera, you would use an image processing algorithm to try and make the colors more vibrant, or remove the blur, or increase the resolution. The output of an image processing algorithm is another image.
And finally there’s also a third category called computer vision, that refers to the process of computing an abstract model given an input image. For example, if you take a picture of a statue of Mario, a vision algorithm would infer the list of (x, y, z) coordinates of the points that make up Mario from the colors at each row and column in the input image.
So to summarize:
Computer Graphics: Convert Model to Image
Image Processing: Refine Image to Image
Computer Vision: Convert Image to Model
Let me explain it using less theory, more intuition.
Let’s consider an Image Processing System and a Computer Vision system. Before going into their details (to make this write up complete), let’s speak about their common notion i.e. manipulating images using classified techniques to produce certain results.
So, image is the common keyword here. Now, lets consider this image below:
For a computer, it’s just a representation of array of numbers. Let’s do an interesting work. What can you say about that image? (think)
…….
……
…..
….
…
..
!
- The image is a multi channel (3, R,G,B) colour image. (can we separa
Let me explain it using less theory, more intuition.
Let’s consider an Image Processing System and a Computer Vision system. Before going into their details (to make this write up complete), let’s speak about their common notion i.e. manipulating images using classified techniques to produce certain results.
So, image is the common keyword here. Now, lets consider this image below:
For a computer, it’s just a representation of array of numbers. Let’s do an interesting work. What can you say about that image? (think)
…….
……
…..
….
…
..
!
- The image is a multi channel (3, R,G,B) colour image. (can we separate each channel?, Shall we convert it into mono channel or gray scale?)
- The image has many standard colours in it (can we separate each of them into separate images?)
- It has horizontal & vertical lines in black colour (can we separate / count them?)
- Those black lines make squares (can we separate them into images ? or, can we count them?)
- There is a white coloured rectangle(can you identify the rectangle and say what it contains?) any guess? is that a bill, or a book or a chart??
- Most of the image has similar colour, and the above mentioned ones form a portion of image (what do you infer from this statement?)
- Now,(Can you detect or separate the book/chart from it’s background to make two more images each containing only foreground details and only background details?)(Can you name the book/chart or recognise it?)
- And, yeah i see a shadow of chart in the right portion of the image (can you say where the light source is present?)
Okay! enough questions. Let’s ask computer to answer them… but wait, can it answer? No way. Ok so, write a software (program) so that computer manipulates the numbers may be then we get the answer.
But.. how? do you know MATLAB? Aware of Image Processing, Computer Vision toolboxes in it?? Fine, it’s ok not knowing them (MATLAB isn’t free either).
Can you code in C/C++/Python? Great! OpenCV is widely accepted & tested open source library so let’s code in C++ (my choice, make your’s). Let’s look at the questions once again.
Answers::
- Colour to Gray scale conversion
- Colour based image_segmentation
- Edge_detection or Hough_Transform based Line detection
- Filling_holes
- It’s a chart
- Chart is placed on a cardboard
- i) Ever used the app CamScanner to scan documents?download this image, go ahead to scan the chart…it’s possible. ii) It is known as Macbeth colour chart. For a computer to recognise a Macbeth chart in a given image, we may need to use machine learning with a good enough data set.
- In computer graphics, object is often reconstructed from known light source model. In the image above, light source is to the left of the camera and object (hence a shadow at the right).
For questions 1 to 3, input and output both are images. For Qn 4, output is a count of image outputs. Such a system taking images as input performing various operations on the image to determine output images or their details is an Image Processing system.
For questions 5 to 8, the input though is an image, outcome of the program is a semantic analysis of the outputs of manipulations done to the input image. Such a system which is used to analyse/modify/process images and in turn use the analysis/modifications/process outcomes to perform any action is a Computer Vision System.
In short,computer vision is a super set of image processing. And computer vision again is a sub set of Artificial Intelligence.
Hope this helps! Thanks for A2A.
Computer graphics is drawing the figure of an apple on your computer screen.
Computer vision is a camera looking at an apple, and the computer being able to figure out that it’s an apple.
Image processing is taking a photo of an apple, and applying filters on it, to post to Instagram, for example.
"Computer Vision" is about computers being able to "see" the human world. IE, a computer has cameras and can run vision recognition algorithms to identity what it's looking at. Popular applications are facial recognition and computer-controlled driving
"visual computing" is about humans being able to "see" the computation. IE, it could refer to visual programming languages (where you "draw" your program as a flowchart rather than "program" it as a textual document). Or it could refer to visualization of results, such as a fancy density chart to help you discern meaning from 1 million data p
"Computer Vision" is about computers being able to "see" the human world. IE, a computer has cameras and can run vision recognition algorithms to identity what it's looking at. Popular applications are facial recognition and computer-controlled driving
"visual computing" is about humans being able to "see" the computation. IE, it could refer to visual programming languages (where you "draw" your program as a flowchart rather than "program" it as a textual document). Or it could refer to visualization of results, such as a fancy density chart to help you discern meaning from 1 million data points ( as opposed to just giving you a million row spreadsheet).
As with the multi-channel blind conversion, we assumed a similar buying pattern. For SR, however, during the calculation the picture is impaired by three factors: blurring, overcoming decimation, and noisy corruption.
Various external factors such as atmospheric turbulence, camera lens, relative camera scene movement, etc, can contribute to the blur.
Yet we assume that we can model them with an unknown point diffusion function (PSF) hk as a convolution. The resolution decimal operator D.) (models the CCD sensor function is performed. It consists of a convolution with a PSF sensor followed by a s
As with the multi-channel blind conversion, we assumed a similar buying pattern. For SR, however, during the calculation the picture is impaired by three factors: blurring, overcoming decimation, and noisy corruption.
Various external factors such as atmospheric turbulence, camera lens, relative camera scene movement, etc, can contribute to the blur.
Yet we assume that we can model them with an unknown point diffusion function (PSF) hk as a convolution. The resolution decimal operator D.) (models the CCD sensor function is performed. It consists of a convolution with a PSF sensor followed by a sampler described by a sum of delta functions placed on a grid as a multiplication. The decimal operator can also contain "warping," which models the geometric transition during acquisition.
The above model is the state of the art as it takes into consideration all potential degradations. The goal of K is to estimate the initial high-resolution image u in K blurred, low-resolution and noisy images zk.
Super resolution requires the identification, which is very difficult to do, of images with sub-pixel precision. Assuming that our model includes unknown PSFs (blurs), this approach is effective against these misalignments. By measuring flushes on the high resolution grid our algorithm automates subpixel registration. A maximum posteriori (MAP) estimator of high-resolution images and blurs is used for alternative minimization (AM) scheme.
The SR app, running on Android 5.0 smartphones is available here (without blind deconvolution). Until loading, you must first unpack the package. Please note that only on Google Nexus 5 was the mobile device checked. These could also run on other Android smartphones 5.0 or later. This is a beta version for testing only. We provide no assistance whatsoever.
For Example :
Specific images taken with a digital standard camera superresolution
- Low Quality Initial Pictures
- Registered roughly and cut
- High resolution picture and PSFs reconstructed
- The best camera zoom picture obtained
Image overflow obtained with a standard digital camera
- Bilinear video series interpolated
- Image series super-resolved