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Blind Image Deblurring

Blind Image Deblurring is a classical problem in image processing and computer vision, which aims to recover a latent image from a blurred input.

Source: Learning a Discriminative Prior for Blind Image Deblurring

Papers

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Showing 26–50 of 70 papers

TitleStatusHype
An Improved Optimal Proximal Gradient Algorithm for Non-Blind Image Deblurring—0
Blind Image Deblurring: a Review—0
Blind Image Deblurring based on Kernel Mixture—0
Blind Image Deblurring by Spectral Properties of Convolution Operators—0
Blind image deblurring using class-adapted image priors—0
Blind Image Deblurring Using Dark Channel Prior—0
Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems—0
Scale Adaptive Blind Deblurring—0
Select Good Regions for Deblurring based on Convolutional Neural Networks—0
Self-Paced Kernel Estimation for Robust Blind Image Deblurring—0
Self-Supervised Multi-Scale Network for Blind Image Deblurring via Alternating Optimization—0
Semi-Blind Image Deblurring Based on Framelet Prior—0
Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior—0
Unsupervised Blind Image Deblurring Based on Self-Enhancement—0
A Comprehensive Survey on Deep Neural Image Deblurring—0
FCL-GAN: A Lightweight and Real-Time Baseline for Unsupervised Blind Image Deblurring—0
Frequency-Aware Guidance for Blind Image Restoration via Diffusion Models—0
Frequency-domain Learning with Kernel Prior for Blind Image Deblurring—0
Graph-Based Blind Image Deblurring From a Single Photograph—0
Image Restoration from Parametric Transformations using Generative Models—0
Kernel Estimation from Salient Structure for Robust Motion Deblurring—0
Learning a Discriminative Prior for Blind Image Deblurring—0
Learning Collaborative Generation Correction Modules for Blind Image Deblurring and Beyond—0
Learning Discriminative Data Fitting Functions for Blind Image Deblurring—0
Learning Spatially-Variant MAP Models for Non-Blind Image Deblurring—0
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