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 511–520 of 3874 papers

TitleStatusHype
Lightweight Image Super-Resolution with Superpixel Token InteractionCode1
Spatial-Frequency Mutual Learning for Face Super-ResolutionCode1
Rethinking Multi-Contrast MRI Super-Resolution: Rectangle-Window Cross-Attention Transformer and Arbitrary-Scale UpsamplingCode1
Compression-Aware Video Super-ResolutionCode1
CutMIB: Boosting Light Field Super-Resolution via Multi-View Image BlendingCode1
Azimuth Super-Resolution for FMCW Radar in Autonomous DrivingCode1
Learning Correction Filter via Degradation-Adaptive Regression for Blind Single Image Super-ResolutionCode1
Toward Stable, Interpretable, and Lightweight Hyperspectral Super-ResolutionCode1
Equivalent Transformation and Dual Stream Network Construction for Mobile Image Super-ResolutionCode1
Deep Arbitrary-Scale Image Super-Resolution via Scale-Equivariance PursuitCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified