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 921–930 of 3874 papers

TitleStatusHype
Degradation Oriented and Regularized Network for Blind Depth Super-ResolutionCode1
Efficient scene text image super-resolution with semantic guidanceCode1
Deep Unfolding Network for Image Super-ResolutionCode1
Bilateral Event Mining and Complementary for Event Stream Super-ResolutionCode1
Self-Conditioned Probabilistic Learning of Video RescalingCode1
Deep Video Super-Resolution using HR Optical Flow EstimationCode1
EigenSR: Eigenimage-Bridged Pre-Trained RGB Learners for Single Hyperspectral Image Super-ResolutionCode1
DeFMO: Deblurring and Shape Recovery of Fast Moving ObjectsCode1
Deformable 3D Convolution for Video Super-ResolutionCode1
Efficient and Degradation-Adaptive Network for Real-World Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified