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 2011–2020 of 3874 papers

TitleStatusHype
Downscaled Representation Matters: Improving Image Rescaling with Collaborative Downscaled Images—0
Downscaling Extreme Rainfall Using Physical-Statistical Generative Adversarial Learning—0
DRFN: Deep Recurrent Fusion Network for Single-Image Super-Resolution with Large Factors—0
DSPO: Direct Semantic Preference Optimization for Real-World Image Super-Resolution—0
D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks—0
DSRGAN: Detail Prior-Assisted Perceptual Single Image Super-Resolution via Generative Adversarial Networks—0
DSSR-Net for Super-Resolution Radar Range Profiles—0
Dual Circle Contrastive Learning-Based Blind Image Super-Resolution—0
Dual-domain Modulation Network for Lightweight Image Super-Resolution—0
Dual Perceptual Loss for Single Image Super-Resolution Using ESRGAN—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified