Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter
Image fusion is a very practical technology that can be applied in many fields, such as medicine, remote sensing and surveillance. An image fusion method using multi-scale decomposition and joint sparse representation is introduced in this paper. First, joint sparse representation is applied to deco...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/1/118 |
| _version_ | 1811183967682953216 |
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| author | Yudan Liu Xiaomin Yang Rongzhu Zhang Marcelo Keese Albertini Turgay Celik Gwanggil Jeon |
| author_facet | Yudan Liu Xiaomin Yang Rongzhu Zhang Marcelo Keese Albertini Turgay Celik Gwanggil Jeon |
| author_sort | Yudan Liu |
| collection | DOAJ |
| description | Image fusion is a very practical technology that can be applied in many fields, such as medicine, remote sensing and surveillance. An image fusion method using multi-scale decomposition and joint sparse representation is introduced in this paper. First, joint sparse representation is applied to decompose two source images into a common image and two innovation images. Second, two initial weight maps are generated by filtering the two source images separately. Final weight maps are obtained by joint bilateral filtering according to the initial weight maps. Then, the multi-scale decomposition of the innovation images is performed through the rolling guide filter. Finally, the final weight maps are used to generate the fused innovation image. The fused innovation image and the common image are combined to generate the ultimate fused image. The experimental results show that our method’s average metrics are: mutual information (<inline-formula> <math display="inline"> <semantics> <mrow> <mi>M</mi> <mi>I</mi> </mrow> </semantics> </math> </inline-formula>)—5.3377, feature mutual information (<inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>M</mi> <mi>I</mi> </mrow> </semantics> </math> </inline-formula>)—0.5600, normalized weighted edge preservation value (<inline-formula> <math display="inline"> <semantics> <msup> <mi>Q</mi> <mrow> <mi>A</mi> <mi>B</mi> <mo>/</mo> <mi>F</mi> </mrow> </msup> </semantics> </math> </inline-formula>)—0.6978 and nonlinear correlation information entropy (<inline-formula> <math display="inline"> <semantics> <mrow> <mi>N</mi> <mi>C</mi> <mi>I</mi> <mi>E</mi> </mrow> </semantics> </math> </inline-formula>)—0.8226. Our method can achieve better performance compared to the state-of-the-art methods in visual perception and objective quantification. |
| first_indexed | 2024-04-11T13:05:32Z |
| format | Article |
| id | doaj.art-fdaef8863fbb4fce93e2c3fac1edc86c |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T13:05:32Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-fdaef8863fbb4fce93e2c3fac1edc86c2022-12-22T04:22:47ZengMDPI AGEntropy1099-43002020-01-0122111810.3390/e22010118e22010118Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance FilterYudan Liu0Xiaomin Yang1Rongzhu Zhang2Marcelo Keese Albertini3Turgay Celik4Gwanggil Jeon5College of Electronics and Information Engineering, Sichuan University, Chengdu 610064, ChinaCollege of Electronics and Information Engineering, Sichuan University, Chengdu 610064, ChinaCollege of Electronics and Information Engineering, Sichuan University, Chengdu 610064, ChinaDepartment of Computer Science, Federal University of Uberlandia, Uberlandia, MG 38408-100, BrazilSchool of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg 2000, South AfricaSchool of Electronic Engineering, Xidian University, Xi’an 710071, ChinaImage fusion is a very practical technology that can be applied in many fields, such as medicine, remote sensing and surveillance. An image fusion method using multi-scale decomposition and joint sparse representation is introduced in this paper. First, joint sparse representation is applied to decompose two source images into a common image and two innovation images. Second, two initial weight maps are generated by filtering the two source images separately. Final weight maps are obtained by joint bilateral filtering according to the initial weight maps. Then, the multi-scale decomposition of the innovation images is performed through the rolling guide filter. Finally, the final weight maps are used to generate the fused innovation image. The fused innovation image and the common image are combined to generate the ultimate fused image. The experimental results show that our method’s average metrics are: mutual information (<inline-formula> <math display="inline"> <semantics> <mrow> <mi>M</mi> <mi>I</mi> </mrow> </semantics> </math> </inline-formula>)—5.3377, feature mutual information (<inline-formula> <math display="inline"> <semantics> <mrow> <mi>F</mi> <mi>M</mi> <mi>I</mi> </mrow> </semantics> </math> </inline-formula>)—0.5600, normalized weighted edge preservation value (<inline-formula> <math display="inline"> <semantics> <msup> <mi>Q</mi> <mrow> <mi>A</mi> <mi>B</mi> <mo>/</mo> <mi>F</mi> </mrow> </msup> </semantics> </math> </inline-formula>)—0.6978 and nonlinear correlation information entropy (<inline-formula> <math display="inline"> <semantics> <mrow> <mi>N</mi> <mi>C</mi> <mi>I</mi> <mi>E</mi> </mrow> </semantics> </math> </inline-formula>)—0.8226. Our method can achieve better performance compared to the state-of-the-art methods in visual perception and objective quantification.https://www.mdpi.com/1099-4300/22/1/118image entropyjoint bilateral filterimage fusionrolling guidance filterjoint sparse representationmulti-scale decomposition |
| spellingShingle | Yudan Liu Xiaomin Yang Rongzhu Zhang Marcelo Keese Albertini Turgay Celik Gwanggil Jeon Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter Entropy image entropy joint bilateral filter image fusion rolling guidance filter joint sparse representation multi-scale decomposition |
| title | Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter |
| title_full | Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter |
| title_fullStr | Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter |
| title_full_unstemmed | Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter |
| title_short | Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter |
| title_sort | entropy based image fusion with joint sparse representation and rolling guidance filter |
| topic | image entropy joint bilateral filter image fusion rolling guidance filter joint sparse representation multi-scale decomposition |
| url | https://www.mdpi.com/1099-4300/22/1/118 |
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