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 3681–3690 of 3874 papers

TitleStatusHype
DaBiT: Depth and Blur informed Transformer for Joint Refocusing and Super-ResolutionCode0
3D Appearance Super-Resolution with Deep LearningCode0
BadRefSR: Backdoor Attacks Against Reference-based Image Super ResolutionCode0
Hyperspectral Super-Resolution via Global-Local Low-Rank Matrix EstimationCode0
Hyperspectral Image Super-Resolution With Optimized RGB GuidanceCode0
Texture and Noise Dual Adaptation for Infrared Image Super-ResolutionCode0
Reference-based Texture transfer for Single Image Super-resolution of Magnetic Resonance imagesCode0
Hyperspectral and multispectral image fusion with arbitrary resolution through self-supervised representationsCode0
CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature DenoiserCode0
Reference-free Axial Super-resolution of 3D Microscopy Images using Implicit Neural Representation with a 2D Diffusion PriorCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified