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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 51–67 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
Unfolding the Alternating Optimization for Blind Super ResolutionCode1
Blind Image Super-Resolution with Spatial Context Hallucination—0
Stochastic Frequency Masking to Improve Super-Resolution and Denoising NetworksCode1
Blind Super-Resolution Kernel Estimation using an Internal-GANCode0
Blind Super-Resolution With Iterative Kernel CorrectionCode0
Blind Image Fusion for Hyperspectral Imaging with the Directional Total VariationCode0
Simple, Accurate, and Robust Nonparametric Blind Super-Resolution—0
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