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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 31–40 of 67 papers

TitleStatusHype
Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolutionCode1
KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local AdjustmentCode1
Unfolding the Alternating Optimization for Blind Super ResolutionCode1
Stochastic Frequency Masking to Improve Super-Resolution and Denoising NetworksCode1
Taming Stable Diffusion for Computed Tomography Blind Super-Resolution—0
Adaptive Blind Super-Resolution Network for Spatial-Specific and Spatial-Agnostic Degradations—0
EAM: Enhancing Anything with Diffusion Transformers for Blind Super-Resolution—0
KernelFusion: Assumption-Free Blind Super-Resolution via Patch Diffusion—0
Boosting Diffusion Guidance via Learning Degradation-Aware Models for Blind Super ResolutionCode0
Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution—0
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