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 991–1000 of 3874 papers

TitleStatusHype
Learning Large-Factor EM Image Super-Resolution with Generative PriorsCode1
Revisiting Spatial-Frequency Information Integration from a Hierarchical Perspective for Panchromatic and Multi-Spectral Image FusionCode1
Image Processing GNN: Breaking Rigidity in Super-Resolution—0
Look-Up Table Compression for Efficient Image RestorationCode1
CFAT: Unleashing Triangular Windows for Image Super-resolutionCode2
Super-Resolution Reconstruction from Bayer-Pattern Spike StreamsCode0
Learning Coupled Dictionaries from Unpaired Data for Image Super-Resolution—0
Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real WorldCode1
CoDe: An Explicit Content Decoupling Framework for Image Restoration—0
CDFormer: When Degradation Prediction Embraces Diffusion Model for Blind Image Super-ResolutionCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified