Image Fusing by Block Compressed Sensing in Contourlet Domain

For traditional image fusion method results in loss of texture detail, a block compressed sensing image fusion algorithm in contourlet domain with a shift invariant and anti-aliasing ability called Contourlet_BCS was proposed. Contourlet_BCS introduced contourlet transform into the sparse representa...

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Main Authors: Aiping Tang, Hui Cao
Format: Article
Language:zho
Published: Beijing Xintong Media Co., Ltd 2015-12-01
Series:Dianxin kexue
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Online Access:http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2015348/
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author Aiping Tang
Hui Cao
author_facet Aiping Tang
Hui Cao
author_sort Aiping Tang
collection DOAJ
description For traditional image fusion method results in loss of texture detail, a block compressed sensing image fusion algorithm in contourlet domain with a shift invariant and anti-aliasing ability called Contourlet_BCS was proposed. Contourlet_BCS introduced contourlet transform into the sparse representation step of compressed sensing since its remarkable feature of articulating the texture and edge information, meanwhile, the low-frequency coefficients fused by weighted rule of regional energy and high-frequency coefficients based on the weighted fusion rule by generalized Gaussian distribution model were also used. Finally, the high quality image can be reconstructed by smooth projection Landweber iteration method under the compressed sensing framework. Experimental results show that the image fused by Contourlet_BCS was better than the traditional method and the fusion image texture clear and had more abundant edge details.
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institution Kabale University
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publishDate 2015-12-01
publisher Beijing Xintong Media Co., Ltd
record_format Article
series Dianxin kexue
spelling doaj-art-3c0c8d7e85f6444cb4e1a98402d7404d2025-01-15T03:15:49ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012015-12-0131768259611489Image Fusing by Block Compressed Sensing in Contourlet DomainAiping TangHui CaoFor traditional image fusion method results in loss of texture detail, a block compressed sensing image fusion algorithm in contourlet domain with a shift invariant and anti-aliasing ability called Contourlet_BCS was proposed. Contourlet_BCS introduced contourlet transform into the sparse representation step of compressed sensing since its remarkable feature of articulating the texture and edge information, meanwhile, the low-frequency coefficients fused by weighted rule of regional energy and high-frequency coefficients based on the weighted fusion rule by generalized Gaussian distribution model were also used. Finally, the high quality image can be reconstructed by smooth projection Landweber iteration method under the compressed sensing framework. Experimental results show that the image fused by Contourlet_BCS was better than the traditional method and the fusion image texture clear and had more abundant edge details.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2015348/block compressed sensingContourlet transformgeneralized Gaussian distributionweighted fusion
spellingShingle Aiping Tang
Hui Cao
Image Fusing by Block Compressed Sensing in Contourlet Domain
Dianxin kexue
block compressed sensing
Contourlet transform
generalized Gaussian distribution
weighted fusion
title Image Fusing by Block Compressed Sensing in Contourlet Domain
title_full Image Fusing by Block Compressed Sensing in Contourlet Domain
title_fullStr Image Fusing by Block Compressed Sensing in Contourlet Domain
title_full_unstemmed Image Fusing by Block Compressed Sensing in Contourlet Domain
title_short Image Fusing by Block Compressed Sensing in Contourlet Domain
title_sort image fusing by block compressed sensing in contourlet domain
topic block compressed sensing
Contourlet transform
generalized Gaussian distribution
weighted fusion
url http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2015348/
work_keys_str_mv AT aipingtang imagefusingbyblockcompressedsensingincontourletdomain
AT huicao imagefusingbyblockcompressedsensingincontourletdomain