Image Reconstruction Based on Combination of Inverse Scattering Technique and Total Variation Regularization Method

In this research, Total Variation (TV) regularization method was incorporated with the Forward-Backward Time-Stepping (FBTS) algorithm to deal with the ill-posedness of the inverse scattering problem in the time domain. The effectiveness between FBTS without and with regularization method is compare...

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Main Author:	Nor Haizan, Binti Jamali
Format:	Thesis
Language:	English
Published:	2020
Subjects:	T Technology (General)
Online Access:	http://ir.unimas.my/id/eprint/30058/4/Image%20Reconstruction%20Based%20on%20Combination%20of%20Inverse%20Scattering.pdf
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id	my-unimas-ir.30058
record_format	uketd_dc
spelling	my-unimas-ir.300582023-04-18T03:25:18Z Image Reconstruction Based on Combination of Inverse Scattering Technique and Total Variation Regularization Method 2020 Nor Haizan, Binti Jamali T Technology (General) TK Electrical engineering. Electronics Nuclear engineering In this research, Total Variation (TV) regularization method was incorporated with the Forward-Backward Time-Stepping (FBTS) algorithm to deal with the ill-posedness of the inverse scattering problem in the time domain. The effectiveness between FBTS without and with regularization method is compared and analyzed by numerical simulations and calculation of Mean Square Error (MSE). Finite-Difference Time-Domain (FDTD) scheme is used to calculate the inverse scattering signals in forward time-stepping and adjoint field in backward time-stepping to reconstruct the microwave properties. The Forward-Backward Time-Stepping - Total Variation (FBTS-TV) regularization algorithm is in a two-dimensional case and implemented in C++ language executed in single computing. The FBTS-TV regularization method shows a good performance of reconstructing relative permittivity and conductivity profiles of the unknown embedded object for its size, shape, and location. The image reconstruction in enhanced by smoothing irregular contours while preserved the edges, and hence produced a better estimation of the image’s boundaries. A distinct improvement is shown in the reconstruction of the object’s relative permittivity. In the case of reconstruction of a simple object for relative permittivity, FBTS-TV improved the FBTS algorithm by 15%. Universiti Malaysia Sarawak, (UNIMAS) 2020 Thesis http://ir.unimas.my/id/eprint/30058/ http://ir.unimas.my/id/eprint/30058/4/Image%20Reconstruction%20Based%20on%20Combination%20of%20Inverse%20Scattering.pdf text en validuser masters Universiti Malaysia Sarawak (UNIMAS) Faculty of Engineering
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	T Technology (General) T Technology (General)
spellingShingle	T Technology (General) T Technology (General) Nor Haizan, Binti Jamali Image Reconstruction Based on Combination of Inverse Scattering Technique and Total Variation Regularization Method
description	In this research, Total Variation (TV) regularization method was incorporated with the Forward-Backward Time-Stepping (FBTS) algorithm to deal with the ill-posedness of the inverse scattering problem in the time domain. The effectiveness between FBTS without and with regularization method is compared and analyzed by numerical simulations and calculation of Mean Square Error (MSE). Finite-Difference Time-Domain (FDTD) scheme is used to calculate the inverse scattering signals in forward time-stepping and adjoint field in backward time-stepping to reconstruct the microwave properties. The Forward-Backward Time-Stepping - Total Variation (FBTS-TV) regularization algorithm is in a two-dimensional case and implemented in C++ language executed in single computing. The FBTS-TV regularization method shows a good performance of reconstructing relative permittivity and conductivity profiles of the unknown embedded object for its size, shape, and location. The image reconstruction in enhanced by smoothing irregular contours while preserved the edges, and hence produced a better estimation of the image’s boundaries. A distinct improvement is shown in the reconstruction of the object’s relative permittivity. In the case of reconstruction of a simple object for relative permittivity, FBTS-TV improved the FBTS algorithm by 15%.
format	Thesis
qualification_level	Master's degree
author	Nor Haizan, Binti Jamali
author_facet	Nor Haizan, Binti Jamali
author_sort	Nor Haizan, Binti Jamali
title	Image Reconstruction Based on Combination of Inverse Scattering Technique and Total Variation Regularization Method
title_short	Image Reconstruction Based on Combination of Inverse Scattering Technique and Total Variation Regularization Method
title_full	Image Reconstruction Based on Combination of Inverse Scattering Technique and Total Variation Regularization Method
title_fullStr	Image Reconstruction Based on Combination of Inverse Scattering Technique and Total Variation Regularization Method
title_full_unstemmed	Image Reconstruction Based on Combination of Inverse Scattering Technique and Total Variation Regularization Method
title_sort	image reconstruction based on combination of inverse scattering technique and total variation regularization method
granting_institution	Universiti Malaysia Sarawak (UNIMAS)
granting_department	Faculty of Engineering
publishDate	2020
url	http://ir.unimas.my/id/eprint/30058/4/Image%20Reconstruction%20Based%20on%20Combination%20of%20Inverse%20Scattering.pdf
_version_	1783728375556210688

Image Reconstruction Based on Combination of Inverse Scattering Technique and Total Variation Regularization Method

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