Image Fusion With Convolutional Sparse Representation

As a popular signal modeling technique, sparse representation (SR) has achieved great success in image fusion over the last few years with a number of effective algorithms being proposed. However, due to the patch-based manner applied in sparse coding, most existing SR-based fusion methods suffer fr...

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Vydáno v:	IEEE signal processing letters Ročník 23; číslo 12; s. 1882 - 1886
Hlavní autoři:	Liu, Yu, Chen, Xun, Ward, Rabab K., Jane Wang, Z.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.12.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Computer vision Convolution Convolutional sparse representation (CSR) Decomposition detail preservation Image fusion Image quality Image sensors Representations shift invariance Signal processing algorithms Signal resolution sparse representation (SR) Transforms Visual observation
ISSN:	1070-9908, 1558-2361
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	As a popular signal modeling technique, sparse representation (SR) has achieved great success in image fusion over the last few years with a number of effective algorithms being proposed. However, due to the patch-based manner applied in sparse coding, most existing SR-based fusion methods suffer from two drawbacks, namely, limited ability in detail preservation and high sensitivity to misregistration, while these two issues are of great concern in image fusion. In this letter, we introduce a recently emerged signal decomposition model known as convolutional sparse representation (CSR) into image fusion to address this problem, which is motivated by the observation that the CSR model can effectively overcome the above two drawbacks. We propose a CSR-based image fusion framework, in which each source image is decomposed into a base layer and a detail layer, for multifocus image fusion and multimodal image fusion. Experimental results demonstrate that the proposed fusion methods clearly outperform the SR-based methods in terms of both objective assessment and visual quality.
AbstractList	As a popular signal modeling technique, sparse representation (SR) has achieved great success in image fusion over the last few years with a number of effective algorithms being proposed. However, due to the patch-based manner applied in sparse coding, most existing SR-based fusion methods suffer from two drawbacks, namely, limited ability in detail preservation and high sensitivity to misregistration, while these two issues are of great concern in image fusion. In this letter, we introduce a recently emerged signal decomposition model known as convolutional sparse representation (CSR) into image fusion to address this problem, which is motivated by the observation that the CSR model can effectively overcome the above two drawbacks. We propose a CSR-based image fusion framework, in which each source image is decomposed into a base layer and a detail layer, for multifocus image fusion and multimodal image fusion. Experimental results demonstrate that the proposed fusion methods clearly outperform the SR-based methods in terms of both objective assessment and visual quality.
Author	Ward, Rabab K. Liu, Yu Chen, Xun Jane Wang, Z.
Author_xml	– sequence: 1 givenname: Yu surname: Liu fullname: Liu, Yu email: yuliu@hfut.edu.cn organization: Department of Biomedical Engineering, Hefei University of Technology, Hefei, China – sequence: 2 givenname: Xun surname: Chen fullname: Chen, Xun email: xun.chen@hfut.edu.cn organization: Department of Biomedical Engineering, Hefei University of Technology, Hefei, China – sequence: 3 givenname: Rabab K. surname: Ward fullname: Ward, Rabab K. email: rababw@ece.ubc.ca organization: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada – sequence: 4 givenname: Z. surname: Jane Wang fullname: Jane Wang, Z. email: zjanew@ece.ubc.ca organization: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
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Snippet	As a popular signal modeling technique, sparse representation (SR) has achieved great success in image fusion over the last few years with a number of...
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SubjectTerms	Algorithms Computer vision Convolution Convolutional sparse representation (CSR) Decomposition detail preservation Image fusion Image quality Image sensors Representations shift invariance Signal processing algorithms Signal resolution sparse representation (SR) Transforms Visual observation
Title	Image Fusion With Convolutional Sparse Representation
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