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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
Learning Detail-Structure Alternative Optimization for Blind Super-ResolutionCode1
Knowledge Distillation based Degradation Estimation for Blind Super-ResolutionCode1
The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution NetworksCode1
Blind Super-Resolution for Remote Sensing Images via Conditional Stochastic Normalizing Flows—0
KXNet: A Model-Driven Deep Neural Network for Blind Super-ResolutionCode1
Joint Learning Content and Degradation Aware Feature for Blind Super-ResolutionCode1
Meta-Learning based Degradation Representation for Blind Super-ResolutionCode1
Learning Generalizable Latent Representations for Novel Degradations in Super Resolution—0
Degradation-Guided Meta-Restoration Network for Blind Super-Resolution—0
A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Bounds—0
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