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 3201–3210 of 3874 papers

TitleStatusHype
Quaternion Wavelet-Conditioned Diffusion Models for Image Super-Resolution—0
QuickSRNet: Plain Single-Image Super-Resolution Architecture for Faster Inference on Mobile Platforms—0
QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution—0
R2LDM: An Efficient 4D Radar Super-Resolution Framework Leveraging Diffusion Model—0
Radar Accurate Localization of UAV Swarms Based on Range Super-Resolution Method—0
Raising The Limit Of Image Rescaling Using Auxiliary Encoding—0
RAISR: Rapid and Accurate Image Super Resolution—0
Random Weights Networks Work as Loss Prior Constraint for Image Restoration—0
RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank—0
Rapid Whole Brain Motion-robust Mesoscale In-vivo MR Imaging using Multi-scale Implicit Neural Representation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified