Multi-Modal Image Fusion Based on Matrix Product State of Tensor

Multi-modal image fusion integrates different images of the same scene collected by different sensors into one image, making the fused image recognizable by the computer and perceived by human vision easily. The traditional tensor decomposition is an approximate decomposition method and has been app...

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Autores principales:	Yixiang Lu, Rui Wang, Qingwei Gao, Dong Sun, De Zhu
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	multi-modal image fusion tensor matrix product state singular value decomposition Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/001dd13641f44a3fa824534f206e322a
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id	oai:doaj.org-article:001dd13641f44a3fa824534f206e322a
record_format	dspace
spelling	oai:doaj.org-article:001dd13641f44a3fa824534f206e322a2021-11-15T06:13:27ZMulti-Modal Image Fusion Based on Matrix Product State of Tensor1662-521810.3389/fnbot.2021.762252https://doaj.org/article/001dd13641f44a3fa824534f206e322a2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnbot.2021.762252/fullhttps://doaj.org/toc/1662-5218Multi-modal image fusion integrates different images of the same scene collected by different sensors into one image, making the fused image recognizable by the computer and perceived by human vision easily. The traditional tensor decomposition is an approximate decomposition method and has been applied to image fusion. In this way, the image details may be lost in the process of fusion image reconstruction. To preserve the fine information of the images, an image fusion method based on tensor matrix product decomposition is proposed to fuse multi-modal images in this article. First, each source image is initialized into a separate third-order tensor. Then, the tensor is decomposed into a matrix product form by using singular value decomposition (SVD), and the Sigmoid function is used to fuse the features extracted in the decomposition process. Finally, the fused image is reconstructed by multiplying all the fused tensor components. Since the algorithm is based on a series of singular value decomposition, a stable closed solution can be obtained and the calculation is also simple. The experimental results show that the fusion image quality obtained by this algorithm is superior to other algorithms in both objective evaluation metrics and subjective evaluation.Yixiang LuRui WangQingwei GaoDong SunDe ZhuFrontiers Media S.A.articlemulti-modalimage fusiontensormatrix product statesingular value decompositionNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neurorobotics, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	multi-modal image fusion tensor matrix product state singular value decomposition Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	multi-modal image fusion tensor matrix product state singular value decomposition Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Yixiang Lu Rui Wang Qingwei Gao Dong Sun De Zhu Multi-Modal Image Fusion Based on Matrix Product State of Tensor
description	Multi-modal image fusion integrates different images of the same scene collected by different sensors into one image, making the fused image recognizable by the computer and perceived by human vision easily. The traditional tensor decomposition is an approximate decomposition method and has been applied to image fusion. In this way, the image details may be lost in the process of fusion image reconstruction. To preserve the fine information of the images, an image fusion method based on tensor matrix product decomposition is proposed to fuse multi-modal images in this article. First, each source image is initialized into a separate third-order tensor. Then, the tensor is decomposed into a matrix product form by using singular value decomposition (SVD), and the Sigmoid function is used to fuse the features extracted in the decomposition process. Finally, the fused image is reconstructed by multiplying all the fused tensor components. Since the algorithm is based on a series of singular value decomposition, a stable closed solution can be obtained and the calculation is also simple. The experimental results show that the fusion image quality obtained by this algorithm is superior to other algorithms in both objective evaluation metrics and subjective evaluation.
format	article
author	Yixiang Lu Rui Wang Qingwei Gao Dong Sun De Zhu
author_facet	Yixiang Lu Rui Wang Qingwei Gao Dong Sun De Zhu
author_sort	Yixiang Lu
title	Multi-Modal Image Fusion Based on Matrix Product State of Tensor
title_short	Multi-Modal Image Fusion Based on Matrix Product State of Tensor
title_full	Multi-Modal Image Fusion Based on Matrix Product State of Tensor
title_fullStr	Multi-Modal Image Fusion Based on Matrix Product State of Tensor
title_full_unstemmed	Multi-Modal Image Fusion Based on Matrix Product State of Tensor
title_sort	multi-modal image fusion based on matrix product state of tensor
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/001dd13641f44a3fa824534f206e322a
work_keys_str_mv	AT yixianglu multimodalimagefusionbasedonmatrixproductstateoftensor AT ruiwang multimodalimagefusionbasedonmatrixproductstateoftensor AT qingweigao multimodalimagefusionbasedonmatrixproductstateoftensor AT dongsun multimodalimagefusionbasedonmatrixproductstateoftensor AT dezhu multimodalimagefusionbasedonmatrixproductstateoftensor
_version_	1718428533568569344

Multi-Modal Image Fusion Based on Matrix Product State of Tensor

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