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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
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
Stochastic Frequency Masking to Improve Super-Resolution and Denoising NetworksCode1
Taming Stable Diffusion for Computed Tomography Blind Super-Resolution—0
Adaptive Blind Super-Resolution Network for Spatial-Specific and Spatial-Agnostic Degradations—0
EAM: Enhancing Anything with Diffusion Transformers for Blind Super-Resolution—0
KernelFusion: Assumption-Free Blind Super-Resolution via Patch Diffusion—0
Boosting Diffusion Guidance via Learning Degradation-Aware Models for Blind Super ResolutionCode0
Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution—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
Image Super-Resolution with Taylor Expansion Approximation and Large Field Reception—0
Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution—0
Blind Image Super-resolution with Rich Texture-Aware Codebooks—0
S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-ResolutionCode0
Scale Guided Hypernetwork for Blind Super-Resolution Image Quality AssessmentCode0
Kernelized Back-Projection Networks for Blind Super Resolution—0
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild—0
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution—0
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