Super-resolution is based on the idea that a combination of low resolution (noisy) sequence of images of a scene can be used to generate a high resolution image or image sequence. Thus it attempts to reconstruct the original scene image with high resolution given a set of observed images at lower resolution..
What are the metrics for super-resolution?
There are two common metrics used to evaluate the image quality after super resolution: Peak Signal to Noise Ratio (PSNR): Structural Similarity Index Measure (SSIM).
What are the super-resolution techniques?
Super-resolution (SR) technique reconstructs a higher-resolution image or sequence from the observed LR images. As SR has been developed for more than three decades, both multi-frame and single-frame SR have significant applications in our daily life..
What is a super-resolution image?
Super-resolution imaging (SR) is a class of techniques that enhance (increase) the resolution of an imaging system. In optical SR the diffraction limit of systems is transcended, while in geometrical SR the resolution of digital imaging sensors is enhanced..
What is image super resolution?
Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image..
What is image super-resolution?
Super-resolution imaging (SR) is a class of techniques that enhance (increase) the resolution of an imaging system. In optical SR the diffraction limit of systems is transcended, while in geometrical SR the resolution of digital imaging sensors is enhanced..
What is super resolution in video processing?
Video Super-Resolution is a computer vision task that aims to increase the resolution of a video sequence, typically from lower to higher resolutions. The goal is to generate high-resolution video frames from low-resolution input, improving the overall quality of the video..
What is the difference between high resolution and super-resolution?
The central aim of Super-Resolution (SR) is to generate a higher resolution image from lower resolution images. High resolution image offers a high pixel density and thereby more details about the original scene..
What is the problem with super-resolution?
The problem of super-resolution is to retrieve a plausible high-resolution version of a low-resolution input, i.e. to reverse the generic degradation process we just described. In this post, we use deep neural networks to perform super-resolution..
What is the super resolution image technique?
Super-resolution imaging (SR) is a class of techniques that enhance (increase) the resolution of an imaging system. In optical SR the diffraction limit of systems is transcended, while in geometrical SR the resolution of digital imaging sensors is enhanced..
What is the super-resolution image technique?
Super-resolution imaging (SR) is a class of techniques that enhance (increase) the resolution of an imaging system. In optical SR the diffraction limit of systems is transcended, while in geometrical SR the resolution of digital imaging sensors is enhanced..
What is ultra high definition imaging?
Increasingly, modern mobile devices allow capturing images at Ultra-High-Definition (UHD) resolution, which includes 4K and 8K images. However, current single image super-resolution (SISR) methods focus on super-resolving images to ones with resolution up to high definition (HD) and ignore higher-resolution UHD images..
Where is super resolution used?
Super-resolution imaging techniques are used in general image processing and in super-resolution microscopy..
Why is image super-resolution important?
The central aim of Super-Resolution (SR) is to generate a higher resolution image from lower resolution images. High resolution image offers a high pixel density and thereby more details about the original scene..
Increasingly, modern mobile devices allow capturing images at Ultra-High-Definition (UHD) resolution, which includes 4K and 8K images. However, current single image super-resolution (SISR) methods focus on super-resolving images to ones with resolution up to high definition (HD) and ignore higher-resolution UHD images.
Super-Resolution (SR) is a branch of Artificial Intelligence (AI) that aims to tackle this problem, whereby a given LR image can be upscaled to retrieve an image with higher resolution and thus more discernible details that can then be used in downstream tasks such as object classification, face recognition, and so on.
The central aim of Super-Resolution (SR) is to generate a higher resolution image from lower resolution images. High resolution image offers a high pixel density and thereby more details about the original scene.
The DIV2K dataset is one of the most popular datasets used for image super-resolution, which is collected for NTIRE2017 and NTIRE2018 Super-Resolution Challenges. The dataset is composed of 800 images for training, 100 images for validation, and 100 images for testing. Each image has a 2K resolution.
There are two common metrics used to evaluate the image quality after super resolution: Peak Signal to Noise Ratio (PSNR): Structural Similarity Index Measure (SSIM)
Video Super-Resolution is a computer vision task that aims to increase the resolution of a video sequence, typically from lower to higher resolutions. The goal is to generate high-resolution video frames from low-resolution input, improving the overall quality of the video.
Abstract: Increasingly, modern mobile devices allow capturing images at Ultra-High-Definition (UHD) resolution, which includes 4K and 8K images.
To explore their performance on UHD images, in this paper, we first introduce two large-scale image datasets, UHDSR4K and UHDSR8K, to benchmark existing SISR
With 70,000 V100 GPU hours of training, we benchmark these methods on 4K and 8K resolution images under seven different settings to provide a set of baseline
License
The UHD4K and UHD8K datasets are released under CC BY-NC-ND license.
Our propsoed UHD4K and UHD8K Datasets
We construct two large-scale 4K and 8K Ultra-High-Definition datasets (i.e., UHD4K and UHD8K) for low-level image enhancement. Each of the datasets provides different settings for image super resolution (SR), image deraining (Derain), low light image enhancement (LLIE), and image reflection removal (IRR).
UHD4K (DR): Image Deraining
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UHD4K (IRR): Image Reflection Removal
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UHD4K (LLIE): Low Light Image Enhancement
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UHD4K (SR): Super-resolution
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UHD8K (DR): Image Deraining
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UHD8K (LLIE): Low Light Image Enhancement
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UHD8K (SR): Super-resolution
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Techniques in optical microscopy that lets image resolutions be unaffected by the diffraction limit
Super-resolution microscopy is a series of techniques in optical microscopy that allow such images to have resolutions higher than those imposed by the diffraction limit, which is due to the diffraction of light. Super-resolution imaging techniques rely on the near-field or on the far-field. Among techniques that rely on the latter are those that improve the resolution only modestly beyond the diffraction-limit, such as confocal microscopy with closed pinhole or aided by computational methods such as deconvolution or detector-based pixel reassignment, the 4Pi microscope, and structured-illumination microscopy technologies such as SIM and SMI.
Super Mario 64 is a platform game developed and published by Nintendo for the Nintendo 64. It was released in Japan and North America in 1996 and PAL regions in 1997. It is the first Super Mario game to feature 3D gameplay, combining traditional Super Mario gameplay, visual style, and characters in a large open world. In the game, Bowser, the primary antagonist of the Super Mario franchise, invades Princess Peach's castle and hides the castle's sources of protection, the Power Stars, in many different worlds inside magical paintings. As Mario, the player collects Power Stars to unlock enough of Princess Peach's castle to get to Bowser and rescue Princess Peach.
Techniques in optical microscopy that lets image resolutions be unaffected by the diffraction limit
Super-resolution microscopy is a series of techniques in optical microscopy that allow such images to have resolutions higher than those imposed by the diffraction limit, which is due to the diffraction of light. Super-resolution imaging techniques rely on the near-field or on the far-field. Among techniques that rely on the latter are those that improve the resolution only modestly beyond the diffraction-limit, such as confocal microscopy with closed pinhole or aided by computational methods such as deconvolution or detector-based pixel reassignment, the 4Pi microscope, and structured-illumination microscopy technologies such as SIM and SMI.
Super Mario 64 is a platform game developed
1996 video game
Super Mario 64 is a platform game developed and published by Nintendo for the Nintendo 64. It was released in Japan and North America in 1996 and PAL regions in 1997. It is the first Super Mario game to feature 3D gameplay, combining traditional Super Mario gameplay, visual style, and characters in a large open world. In the game, Bowser, the primary antagonist of the Super Mario franchise, invades Princess Peach's castle and hides the castle's sources of protection, the Power Stars, in many different worlds inside magical paintings. As Mario, the player collects Power Stars to unlock enough of Princess Peach's castle to get to Bowser and rescue Princess Peach.
