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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
Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded ImagesCode1
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
Meta-Learned Kernel For Blind Super-Resolution Kernel EstimationCode1
Learning Detail-Structure Alternative Optimization for Blind Super-ResolutionCode1
Knowledge Distillation based Degradation Estimation for Blind Super-ResolutionCode1
The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution NetworksCode1
Blind Super-Resolution for Remote Sensing Images via Conditional Stochastic Normalizing Flows—0
KXNet: A Model-Driven Deep Neural Network for Blind Super-ResolutionCode1
Joint Learning Content and Degradation Aware Feature for Blind Super-ResolutionCode1
Meta-Learning based Degradation Representation for Blind Super-ResolutionCode1
Learning Generalizable Latent Representations for Novel Degradations in Super Resolution—0
Degradation-Guided Meta-Restoration Network for Blind Super-Resolution—0
A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Bounds—0
Unsupervised Blur Kernel Estimation and Correction for Blind Super-ResolutionCode0
Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution PriorsCode2
Deep Constrained Least Squares for Blind Image Super-ResolutionCode2
A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net DiscriminatorsCode1
Kernel-aware Burst Blind Super-ResolutionCode0
Pixel-Level Kernel Estimation for Blind Super-ResolutionCode0
Blind inverse problems with isolated spikes—0
Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus ImagingCode0
Temporal Kernel Consistency for Blind Video Super-Resolution—0
Finding Discriminative Filters for Specific Degradations in Blind Super-ResolutionCode1
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