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 3511–3520 of 3874 papers

TitleStatusHype
Pixel Recursive Super ResolutionCode0
Learning Accurate and Enriched Features for Stereo Image Super-ResolutionCode0
Edge-guided and Cross-scale Feature Fusion Network for Efficient Multi-contrast MRI Super-ResolutionCode0
PLAIN: Scalable Estimation Architecture for Integrated Sensing and CommunicationCode0
Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution ImagingCode0
SLVR: Super-Light Visual Reconstruction via Blueprint Controllable Convolutions and Exploring Feature Diversity RepresentationCode0
s-LWSR: Super Lightweight Super-Resolution NetworkCode0
LD-GAN: Low-Dimensional Generative Adversarial Network for Spectral Image Generation with Variance RegularizationCode0
Plug-and-Play Linear Attention for Pre-trained Image and Video Restoration ModelsCode0
EDADepth: Enhanced Data Augmentation for Monocular Depth EstimationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified