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 611–620 of 3874 papers

TitleStatusHype
Learning Local Implicit Fourier Representation for Image WarpingCode1
Variational Deep Image RestorationCode1
Structured Sparsity Learning for Efficient Video Super-ResolutionCode1
RPLHR-CT Dataset and Transformer Baseline for Volumetric Super-Resolution from CT ScansCode1
Real-World Light Field Image Super-Resolution via Degradation ModulationCode1
Hypernetwork-Based Adaptive Image RestorationCode1
Real-World Image Super-Resolution by Exclusionary Dual-LearningCode1
Recurrent Video Restoration Transformer with Guided Deformable AttentionCode1
ShuffleMixer: An Efficient ConvNet for Image Super-ResolutionCode1
Deep Posterior Distribution-based Embedding for Hyperspectral Image Super-resolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified