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 2191–2200 of 3874 papers

TitleStatusHype
Feature Representation Matters: End-to-End Learning for Reference-based Image Super-resolution—0
Feature Super-Resolution Based Facial Expression Recognition for Multi-scale Low-Resolution Faces—0
Feature Super-Resolution: Make Machine See More Clearly—0
Federated Learning for Blind Image Super-Resolution—0
Feedback Graph Attention Convolutional Network for Medical Image Enhancement—0
Feedback Neural Network based Super-resolution of DEM for generating high fidelity features—0
Feedback Pyramid Attention Networks for Single Image Super-Resolution—0
Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer—0
A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective—0
FFEINR: Flow Feature-Enhanced Implicit Neural Representation for Spatio-temporal Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified