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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 26–50 of 67 papers

TitleStatusHype
The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution NetworksCode1
Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolutionCode1
Unfolding the Alternating Optimization for Blind Super ResolutionCode1
Unsupervised Degradation Representation Learning for Blind Super-ResolutionCode1
Unsupervised Imaging Inverse Problems with Diffusion Distribution MatchingCode1
Degradation-Aware Self-Attention Based Transformer for Blind Image Super-ResolutionCode1
A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net DiscriminatorsCode1
Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIsCode1
End-to-end Alternating Optimization for Blind Super ResolutionCode1
Scale Guided Hypernetwork for Blind Super-Resolution Image Quality AssessmentCode0
Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus ImagingCode0
Boosting Diffusion Guidance via Learning Degradation-Aware Models for Blind Super ResolutionCode0
Blind Super-Resolution With Iterative Kernel CorrectionCode0
Blind Image Fusion for Hyperspectral Imaging with the Directional Total VariationCode0
Unsupervised Blur Kernel Estimation and Correction for Blind Super-ResolutionCode0
Blind Super-Resolution Kernel Estimation using an Internal-GANCode0
Kernel-aware Burst Blind Super-ResolutionCode0
Pixel-Level Kernel Estimation for Blind Super-ResolutionCode0
S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-ResolutionCode0
From General to Specific: Online Updating for Blind Super-Resolution—0
A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Bounds—0
Adaptive Blind Super-Resolution Network for Spatial-Specific and Spatial-Agnostic Degradations—0
Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution—0
Blind Image Super-resolution with Rich Texture-Aware Codebooks—0
Blind Image Super-Resolution with Spatial Context Hallucination—0
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