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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 1–50 of 67 papers

TitleStatusHype
Exploiting Diffusion Prior for Real-World Image Super-ResolutionCode4
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic DataCode4
Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution PriorsCode2
A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-ResolutionCode2
Deep Constrained Least Squares for Blind Image Super-ResolutionCode2
AddSR: Accelerating Diffusion-based Blind Super-Resolution with Adversarial Diffusion DistillationCode2
Knowledge Distillation based Degradation Estimation for Blind Super-ResolutionCode1
KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local AdjustmentCode1
KXNet: A Model-Driven Deep Neural Network for Blind Super-ResolutionCode1
Learning Detail-Structure Alternative Optimization for Blind Super-ResolutionCode1
Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolutionCode1
Meta-Learned Kernel For Blind Super-Resolution Kernel EstimationCode1
Meta-Learning based Degradation Representation for Blind Super-ResolutionCode1
Stochastic Frequency Masking to Improve Super-Resolution and Denoising NetworksCode1
RFSR: Improving ISR Diffusion Models via Reward Feedback LearningCode1
StarSRGAN: Improving Real-World Blind Super-ResolutionCode1
Conditional Hyper-Network for Blind Super-Resolution with Multiple DegradationsCode1
Deep Blind Super-Resolution for Satellite VideoCode1
The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution NetworksCode1
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
Unsupervised Imaging Inverse Problems with Diffusion Distribution MatchingCode1
End-to-end Alternating Optimization for Blind Super ResolutionCode1
End-to-end Alternating Optimization for Real-World Blind Super ResolutionCode1
Finding Discriminative Filters for Specific Degradations in Blind Super-ResolutionCode1
Flow-based Kernel Prior with Application to Blind Super-ResolutionCode1
Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder ApproachCode1
Unsupervised Degradation Representation Learning for Blind Super-ResolutionCode1
Joint Learning Content and Degradation Aware Feature for Blind Super-ResolutionCode1
Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded ImagesCode1
Tackling the Ill-Posedness of Super-Resolution Through Adaptive Target GenerationCode1
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo SimulationCode1
Unfolding the Alternating Optimization for Blind Super ResolutionCode1
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
Blind inverse problems with isolated spikes—0
Blind Super-Resolution for Remote Sensing Images via Conditional Stochastic Normalizing Flows—0
Blind Time-of-Flight Imaging: Sparse Deconvolution on the Continuum with Unknown Kernels—0
Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution—0
Degradation-Guided Meta-Restoration Network for Blind Super-Resolution—0
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild—0
EAM: Enhancing Anything with Diffusion Transformers for Blind Super-Resolution—0
Image Super-Resolution with Taylor Expansion Approximation and Large Field Reception—0
KernelFusion: Assumption-Free Blind Super-Resolution via Patch Diffusion—0
Kernelized Back-Projection Networks for Blind Super Resolution—0
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