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 141–150 of 3874 papers

TitleStatusHype
CoSeR: Bridging Image and Language for Cognitive Super-ResolutionCode2
Swift Parameter-free Attention Network for Efficient Super-ResolutionCode2
Diffusion Models for Image Restoration and Enhancement -- A Comprehensive SurveyCode2
Dual Aggregation Transformer for Image Super-ResolutionCode2
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth EstimationCode2
Efficient Mixed Transformer for Single Image Super-ResolutionCode2
Denoising Diffusion Models for Plug-and-Play Image RestorationCode2
Bicubic++: Slim, Slimmer, Slimmest -- Designing an Industry-Grade Super-Resolution NetworkCode2
Enhancing Video Super-Resolution via Implicit Resampling-based AlignmentCode2
Omni Aggregation Networks for Lightweight Image Super-ResolutionCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified