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Image Deblurring with Compressive Sensing

Zede Journal of Ethiopian Engineers and Architects
Compressive sensing is a technique that enables recovery of signals represented by an underdetermined system of equations. Such a recovery of an original signal is made possible if the samples are represented in a sparse manner provided an appropriate measuring matrix is used for the modelled system.
Dibeya, Rahel Berhanu   +1 more
openaire   +2 more sources

Image deblurring

2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2017
Fu-Wen Yang, Hwei-Jen Lin, Hua Chuang
openaire   +1 more source

PSF estimation for image deblurring

CVGIP: Graphical Models and Image Processing, 1991
Abstract This paper addresses the problem of estimating the point spread function (PSF) of a spatially invariant linear system through which an image has been blurred. This is done by searching for specific features of the original image, such as sharp edge elements or microtexture, from which the blur effect can be identified and the PSF estimated ...
openaire   +1 more source

Deep Idempotent Network for Efficient Single Image Blind Deblurring

IEEE Transactions on Circuits and Systems for Video Technology, 2023
Xin Yu, Yuchao Dai, Zhexiong Wan
exaly  

Defocus Image Deblurring Network With Defocus Map Estimation as Auxiliary Task

IEEE Transactions on Image Processing, 2022
Shaojun Liu, Haoyu Ma, Jing-Hao Xue
exaly  

Surface-Aware Blind Image Deblurring

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
Tieyong Zeng, Ming Yan
exaly  

Blind Remote Sensing Image Deblurring Using Local Binary Pattern Prior

Remote Sensing, 2022
Liangliang Zheng, Ziyu Zhang, Tan Gao
exaly  

Deblurring Images

2006
Per Christian Hansen   +2 more
openaire   +1 more source

Blind Image Deblurring via a Novel Sparse Channel Prior

Mathematics, 2022
Hefeng Yin, Xiao-Jun Wu, Dayi Yang
exaly  

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