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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
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
Multi-scale Frequency Enhancement Network for Blind Image Deblurring—0
NBD-GAP: Non-Blind Image Deblurring Without Clean Target Images—0
Noise-Blind Image Deblurring—0
Non-Uniform Blind Deblurring by Reblurring—0
Non-uniform Motion Deblurring with Blurry Component Divided Guidance—0
NSD-DIL: Null-Shot Deblurring Using Deep Identity Learning—0
OID: Outlier Identifying and Discarding in Blind Image Deblurring—0
Phase-only Image Based Kernel Estimation for Single-image Blind Deblurring—0
Phase-Only Image Based Kernel Estimation for Single Image Blind Deblurring—0
Plug-and-Play Posterior Sampling for Blind Inverse Problems—0
Point spread function estimation for blind image deblurring problems based on framelet transform—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
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