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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 51–60 of 67 papers

TitleStatusHype
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic DataCode4
From General to Specific: Online Updating for Blind Super-Resolution—0
Learning the Non-Differentiable Optimization for Blind Super-Resolution—0
Tackling the Ill-Posedness of Super-Resolution Through Adaptive Target GenerationCode1
End-to-end Alternating Optimization for Blind Super ResolutionCode1
Conditional Hyper-Network for Blind Super-Resolution with Multiple DegradationsCode1
Unsupervised Degradation Representation Learning for Blind Super-ResolutionCode1
Flow-based Kernel Prior with Application to Blind Super-ResolutionCode1
Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolutionCode1
KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local AdjustmentCode1
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