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 871–880 of 3874 papers

TitleStatusHype
Learning Spatial Attention for Face Super-ResolutionCode1
Accelerating Diffusion Models for Inverse Problems through Shortcut SamplingCode1
Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluationCode1
Implicit Grid Convolution for Multi-Scale Image Super-ResolutionCode1
Implicit Neural Image StitchingCode1
Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion ModelsCode1
Robust Single-Image Super-Resolution via CNNs and TV-TV MinimizationCode1
Implicit Transformer Network for Screen Content Image Continuous Super-ResolutionCode1
Learning Spatiotemporal Frequency-Transformer for Low-Quality Video Super-ResolutionCode1
Deep Semantic Statistics Matching (D2SM) Denoising NetworkCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified