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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
Learning Generalizable Latent Representations for Novel Degradations in Super Resolution—0
Learning the Non-Differentiable Optimization for Blind Super-Resolution—0
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution—0
Simple, Accurate, and Robust Nonparametric Blind Super-Resolution—0
Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution—0
Taming Stable Diffusion for Computed Tomography Blind Super-Resolution—0
Temporal Kernel Consistency for Blind Video Super-Resolution—0
Unsupervised Blur Kernel Estimation and Correction for Blind Super-ResolutionCode0
Kernel-aware Burst Blind Super-ResolutionCode0
Blind Super-Resolution Kernel Estimation using an Internal-GANCode0
Blind Image Fusion for Hyperspectral Imaging with the Directional Total VariationCode0
Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus ImagingCode0
Scale Guided Hypernetwork for Blind Super-Resolution Image Quality AssessmentCode0
Boosting Diffusion Guidance via Learning Degradation-Aware Models for Blind Super ResolutionCode0
Blind Super-Resolution With Iterative Kernel CorrectionCode0
S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-ResolutionCode0
Pixel-Level Kernel Estimation for Blind Super-ResolutionCode0
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