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 1621–1630 of 3874 papers

TitleStatusHype
Deep Random Projector: Accelerated Deep Image PriorCode1
Azimuth Super-Resolution for FMCW Radar in Autonomous DrivingCode1
Deep Arbitrary-Scale Image Super-Resolution via Scale-Equivariance PursuitCode1
Metadata-Based RAW Reconstruction via Implicit Neural Functions—0
CutMIB: Boosting Light Field Super-Resolution via Multi-View Image BlendingCode1
Cross-Guided Optimization of Radiance Fields With Multi-View Image Super-Resolution for High-Resolution Novel View Synthesis—0
Correspondence Transformers With Asymmetric Feature Learning and Matching Flow Super-ResolutionCode0
Toward Stable, Interpretable, and Lightweight Hyperspectral Super-ResolutionCode1
Toward Accurate Post-Training Quantization for Image Super ResolutionCode0
Spectral Bayesian Uncertainty for Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified