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 3031–3040 of 3874 papers

TitleStatusHype
SelFSR: Self-Conditioned Face Super-Resolution in the Wild via Flow Field Degradation Network—0
Zoom to Learn, Learn to Zoom—0
Self Super-Resolution for Magnetic Resonance Images using Deep Networks—0
Why Are Deep Representations Good Perceptual Quality Features?—0
Self-supervised arbitrary scale super-resolution framework for anisotropic MRI—0
Self-Supervised Burst Super-Resolution—0
A Progressive Image Restoration Network for High-order Degradation Imaging in Remote Sensing—0
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution—0
A Preliminary Exploration Towards General Image Restoration—0
Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion Generation Model—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified