SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

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Showing 2921–2930 of 3874 papers

TitleStatusHype
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report—0
A Survey of Super-Resolution in Iris Biometrics with Evaluation of Dictionary-Learning—0
A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision—0
Revisiting L1 Loss in Super-Resolution: A Probabilistic View and Beyond—0
A survey of machine learning-based physics event generation—0
A super-resolution reconstruction method for lightweight building images based on an expanding feature modulation network—0
A Study of Efficient Light Field Subsampling and Reconstruction Strategies—0
A Study in Dataset Pruning for Image Super-Resolution—0
Untrained, physics-informed neural networks for structured illumination microscopy—0
Astronomical Image Colorization and upscaling with Generative Adversarial Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified