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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 51–70 of 70 papers

TitleStatusHype
Phase-only Image Based Kernel Estimation for Single-image Blind Deblurring—0
Deblurring Natural Image Using Super-Gaussian Fields—0
Learning Collaborative Generation Correction Modules for Blind Image Deblurring and Beyond—0
Learning Deep Gradient Descent Optimization for Image DeconvolutionCode0
Learning a Discriminative Prior for Blind Image Deblurring—0
Graph-Based Blind Image Deblurring From a Single Photograph—0
Blind Image Deblurring via Reweighted Graph Total Variation—0
Blind Image Deblurring Using Row-Column Sparse Representations—0
Blind Image Deblurring With Outlier Handling—0
Self-Paced Kernel Estimation for Robust Blind Image Deblurring—0
Non-Uniform Blind Deblurring by Reblurring—0
Learning Discriminative Data Fitting Functions for Blind Image Deblurring—0
Blind image deblurring using class-adapted image priors—0
Noise-Blind Image Deblurring—0
Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems—0
Blind Image Deblurring Using Dark Channel Prior—0
Scale Adaptive Blind Deblurring—0
Cascades of Regression Tree Fields for Image Restoration—0
Kernel Estimation from Salient Structure for Robust Motion Deblurring—0
Blind Image Deblurring by Spectral Properties of Convolution Operators—0
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