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 2231–2240 of 3874 papers

TitleStatusHype
Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser—0
Uncertainty-Driven Loss for Single Image Super-Resolution—0
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
Revisiting Temporal Alignment for Video RestorationCode1
SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-ResolutionCode1
SwiftSRGAN -- Rethinking Super-Resolution for Efficient and Real-time Inference—0
Performance of a GPU- and Time-Efficient Pseudo 3D Network for Magnetic Resonance Image Super-Resolution and Motion Artifact Reduction—0
ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image PriorCode1
A Practical Contrastive Learning Framework for Single-Image Super-ResolutionCode1
Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified