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 1671–1680 of 3874 papers

TitleStatusHype
Double U-Net for Super-Resolution and Segmentation of Live Cell Images—0
Learning Detail-Structure Alternative Optimization for Blind Super-ResolutionCode1
Bridging Component Learning with Degradation Modelling for Blind Image Super-ResolutionCode1
DiTBN: Detail Injection-Based Two-Branch Network for Pansharpening of Remote Sensing ImagesCode0
Downscaling Extreme Rainfall Using Physical-Statistical Generative Adversarial Learning—0
Global Learnable Attention for Single Image Super-ResolutionCode1
Super-resolution of positive near-colliding point sources—0
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space ModelCode4
MrSARP: A Hierarchical Deep Generative Prior for SAR Image Super-resolution—0
Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantificationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified