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 2151–2160 of 3874 papers

TitleStatusHype
DAEs for Linear Inverse Problems: Improved Recovery with Provable Guarantees—0
D2C-SR: A Divergence to Convergence Approach for Image Super-Resolution—0
CycMuNet+: Cycle-Projected Mutual Learning for Spatial-Temporal Video Super-Resolution—0
Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution—0
Linearized ADMM and Fast Nonlocal Denoising for Efficient Plug-and-Play Restoration—0
CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data—0
Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems—0
Little Pilot is Needed for Channel Estimation with Integrated Super-Resolution Sensing and Communication—0
CWT-Net: Super-resolution of Histopathology Images Using a Cross-scale Wavelet-based Transformer—0
LLV-FSR: Exploiting Large Language-Vision Prior for Face Super-resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified