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Blind Super-Resolution

Blind Super-Resolution is an image processing technique that aims to reconstruct high-resolution images from low-resolution counterparts without prior knowledge of the degradation process.

Papers

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Showing 21–30 of 67 papers

TitleStatusHype
S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-ResolutionCode0
StarSRGAN: Improving Real-World Blind Super-ResolutionCode1
Scale Guided Hypernetwork for Blind Super-Resolution Image Quality AssessmentCode0
Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIsCode1
Exploiting Diffusion Prior for Real-World Image Super-ResolutionCode4
Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded ImagesCode1
Kernelized Back-Projection Networks for Blind Super Resolution—0
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild—0
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution—0
Meta-Learned Kernel For Blind Super-Resolution Kernel EstimationCode1
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