X-rays Image reconstruction using Proximal Algorithm and adapted TV Regularization

Computed tomography (CT) aims to reconstruct an internal distribution of an object based on projection measurements. In the case of a limited number of projections, the reconstruction problem becomes significantly ill-posed. Practically, reconstruction algorithms play a crucial role in overcoming th...

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Autores principales:	Allag Aicha, Drai Redouane, Boutkedjirt Tarek, Benammar Abdessalam, Djerir Wahiba
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	reconstruction regularization proximal method x-rays ill-posed problem Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/239a0e01f2f2467abf0cdc1811ffc1a0
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id	oai:doaj.org-article:239a0e01f2f2467abf0cdc1811ffc1a0
record_format	dspace
spelling	oai:doaj.org-article:239a0e01f2f2467abf0cdc1811ffc1a02021-12-02T17:13:38ZX-rays Image reconstruction using Proximal Algorithm and adapted TV Regularization2261-236X10.1051/matecconf/202134801011https://doaj.org/article/239a0e01f2f2467abf0cdc1811ffc1a02021-01-01T00:00:00Zhttps://www.matec-conferences.org/articles/matecconf/pdf/2021/17/matecconf_inbes2021_01011.pdfhttps://doaj.org/toc/2261-236XComputed tomography (CT) aims to reconstruct an internal distribution of an object based on projection measurements. In the case of a limited number of projections, the reconstruction problem becomes significantly ill-posed. Practically, reconstruction algorithms play a crucial role in overcoming this problem. In the case of missing or incomplete data, and in order to improve the quality of the reconstruction image, the choice of a sparse regularisation by adding l1 norm is needed. The reconstruction problem is then based on using proximal operators. We are interested in the Douglas-Rachford method and employ total variation (TV) regularization. An efficient technique based on these concepts is proposed in this study. The primary goal is to achieve high-quality reconstructed images in terms of PSNR parameter and relative error. The numerical simulation results demonstrate that the suggested technique minimizes noise and artifacts while preserving structural information. The results are encouraging and indicate the effectiveness of the proposed strategy.Allag AichaDrai RedouaneBoutkedjirt TarekBenammar AbdessalamDjerir WahibaEDP Sciencesarticlereconstructionregularizationproximal methodx-raysill-posed problemEngineering (General). Civil engineering (General)TA1-2040ENFRMATEC Web of Conferences, Vol 348, p 01011 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	reconstruction regularization proximal method x-rays ill-posed problem Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	reconstruction regularization proximal method x-rays ill-posed problem Engineering (General). Civil engineering (General) TA1-2040 Allag Aicha Drai Redouane Boutkedjirt Tarek Benammar Abdessalam Djerir Wahiba X-rays Image reconstruction using Proximal Algorithm and adapted TV Regularization
description	Computed tomography (CT) aims to reconstruct an internal distribution of an object based on projection measurements. In the case of a limited number of projections, the reconstruction problem becomes significantly ill-posed. Practically, reconstruction algorithms play a crucial role in overcoming this problem. In the case of missing or incomplete data, and in order to improve the quality of the reconstruction image, the choice of a sparse regularisation by adding l1 norm is needed. The reconstruction problem is then based on using proximal operators. We are interested in the Douglas-Rachford method and employ total variation (TV) regularization. An efficient technique based on these concepts is proposed in this study. The primary goal is to achieve high-quality reconstructed images in terms of PSNR parameter and relative error. The numerical simulation results demonstrate that the suggested technique minimizes noise and artifacts while preserving structural information. The results are encouraging and indicate the effectiveness of the proposed strategy.
format	article
author	Allag Aicha Drai Redouane Boutkedjirt Tarek Benammar Abdessalam Djerir Wahiba
author_facet	Allag Aicha Drai Redouane Boutkedjirt Tarek Benammar Abdessalam Djerir Wahiba
author_sort	Allag Aicha
title	X-rays Image reconstruction using Proximal Algorithm and adapted TV Regularization
title_short	X-rays Image reconstruction using Proximal Algorithm and adapted TV Regularization
title_full	X-rays Image reconstruction using Proximal Algorithm and adapted TV Regularization
title_fullStr	X-rays Image reconstruction using Proximal Algorithm and adapted TV Regularization
title_full_unstemmed	X-rays Image reconstruction using Proximal Algorithm and adapted TV Regularization
title_sort	x-rays image reconstruction using proximal algorithm and adapted tv regularization
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/239a0e01f2f2467abf0cdc1811ffc1a0
work_keys_str_mv	AT allagaicha xraysimagereconstructionusingproximalalgorithmandadaptedtvregularization AT drairedouane xraysimagereconstructionusingproximalalgorithmandadaptedtvregularization AT boutkedjirttarek xraysimagereconstructionusingproximalalgorithmandadaptedtvregularization AT benammarabdessalam xraysimagereconstructionusingproximalalgorithmandadaptedtvregularization AT djerirwahiba xraysimagereconstructionusingproximalalgorithmandadaptedtvregularization
_version_	1718381309386031104

X-rays Image reconstruction using Proximal Algorithm and adapted TV Regularization

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