Recent advances via convolutional sparse representation model for pixel-level image fusion

Image fusion aims to integrate complementary information from different source images into the final output image. This plays a significant role in high-level vision tasks. However, image fusion methods based on sparse representation (SR) or conventional multiscale transform (MST) have some drawback...

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Published in:	Multimedia tools and applications Vol. 83; no. 17; pp. 52899 - 52930
Main Authors:	Pan, Yue, Lan, Tianye, Xu, Chongyang, Zhang, Chengfang, Feng, Ziliang
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.05.2024 Springer Nature B.V
Subjects:	Algorithms Computer Communication Networks Computer Science Computer vision Data Structures and Information Theory Decomposition Deep learning Dictionaries Infrared imagery Medical imaging Medical research Multimedia Multimedia Information Systems Performance evaluation Pixels Representations Special Purpose and Application-Based Systems Track 6: Computer Vision for Multimedia Applications Trends Wavelet transforms Online convolutional sparse coding Convolutional sparse representation Online convolutional dictionary learning Convolutional dictionary learning Image fusion
ISSN:	1573-7721, 1380-7501, 1573-7721
Online Access:	Get full text
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Abstract	Image fusion aims to integrate complementary information from different source images into the final output image. This plays a significant role in high-level vision tasks. However, image fusion methods based on sparse representation (SR) or conventional multiscale transform (MST) have some drawbacks that are difficult to overcome. As an alternative form of SR, convolutional sparse representation (CSR) has the advantages of detail preservation and shift-invariance, which can overcome the shortcomings of SR- and MST-based fusion methods. Since CSR has been widely used in the field of image fusion and has advanced this field to a great extent, it is necessary to conduct a comprehensive investigation of image fusion based on CSR. To the best of our knowledge, there are no previous papers reviewing and evaluating CSR-based fusion methods, and this study is the first retrospective. In this paper, we focus on CSR-based image fusion methods and review the recent advances in pixel-level image fusion based on CSR. In the experimental part of the paper, multifocal images, infrared-visible images, and multimodal medical images are used as test images to compare and evaluate the performance of different image fusion methods. In addition, the future trend of CSR-based image fusion is discussed. This paper is expected to serve as a resource of reference for both researchers and general learners seeking an overview of CSR-based image fusion.
AbstractList	Image fusion aims to integrate complementary information from different source images into the final output image. This plays a significant role in high-level vision tasks. However, image fusion methods based on sparse representation (SR) or conventional multiscale transform (MST) have some drawbacks that are difficult to overcome. As an alternative form of SR, convolutional sparse representation (CSR) has the advantages of detail preservation and shift-invariance, which can overcome the shortcomings of SR- and MST-based fusion methods. Since CSR has been widely used in the field of image fusion and has advanced this field to a great extent, it is necessary to conduct a comprehensive investigation of image fusion based on CSR. To the best of our knowledge, there are no previous papers reviewing and evaluating CSR-based fusion methods, and this study is the first retrospective. In this paper, we focus on CSR-based image fusion methods and review the recent advances in pixel-level image fusion based on CSR. In the experimental part of the paper, multifocal images, infrared-visible images, and multimodal medical images are used as test images to compare and evaluate the performance of different image fusion methods. In addition, the future trend of CSR-based image fusion is discussed. This paper is expected to serve as a resource of reference for both researchers and general learners seeking an overview of CSR-based image fusion.
Author	Pan, Yue Lan, Tianye Xu, Chongyang Zhang, Chengfang Feng, Ziliang
Author_xml	– sequence: 1 givenname: Yue surname: Pan fullname: Pan, Yue organization: College of Computer Science, Sichuan University – sequence: 2 givenname: Tianye surname: Lan fullname: Lan, Tianye organization: College of Computer Science, Sichuan University – sequence: 3 givenname: Chongyang surname: Xu fullname: Xu, Chongyang organization: College of Computer Science, Sichuan University – sequence: 4 givenname: Chengfang surname: Zhang fullname: Zhang, Chengfang email: chengfangzhang@scpolicec.edu.cn organization: Intelligent Policing Key Laboratory of Sichuan Province, Sichuan Police College – sequence: 5 givenname: Ziliang surname: Feng fullname: Feng, Ziliang organization: College of Computer Science, Sichuan University
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Snippet	Image fusion aims to integrate complementary information from different source images into the final output image. This plays a significant role in high-level...
SourceID	proquest crossref springer
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	52899
SubjectTerms	Algorithms Computer Communication Networks Computer Science Computer vision Data Structures and Information Theory Decomposition Deep learning Dictionaries Infrared imagery Medical imaging Medical research Multimedia Multimedia Information Systems Performance evaluation Pixels Representations Special Purpose and Application-Based Systems Track 6: Computer Vision for Multimedia Applications Trends Wavelet transforms
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Title	Recent advances via convolutional sparse representation model for pixel-level image fusion
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