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A Novel Fusion Strategy and Convolutional Sparse Coding for Robot Multisource Image Fusion

A Novel Fusion Strategy and Convolutional Sparse Coding for Robot Multisource Image Fusion Image fusion refers to the fusion of images collected by multiple sensors about the same target or scene into one image by image processing technology. In this way, the advantages of multiple sensors can be effectively utilized to obtain more comprehensive feature information of the target or scene, which is conducive to human eye observation and subsequent recognition and processing. The traditional fusion methods are easy to lose the image details, resulting in poor fusion effect. Therefore, this paper proposes a multisource image fusion method based on convolutional sparse coding and a novel fusion strategy. Firstly, the image is decomposed into low-rank and sparse through low-rank decomposition. Then, the sparse part is convoluted and decomposed to obtain a set of sparse filter dictionaries, which are applied to image fusion by convolutional sparse coding. The regional energy-Cauchy fuzzy function rule is adopted for low-rank components. The regional Laplace energy is used for sparse component. Finally, the weighted average method is used to obtain the final fusion result. Experimental results show that the proposed method achieves good results in terms of visual effects and objective evaluation indexes. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automatic Control and Computer Sciences Springer Journals

A Novel Fusion Strategy and Convolutional Sparse Coding for Robot Multisource Image Fusion

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References (27)

Publisher
Springer Journals
Copyright
Copyright © Allerton Press, Inc. 2023. ISSN 0146-4116, Automatic Control and Computer Sciences, 2023, Vol. 57, No. 2, pp. 185–195. © Allerton Press, Inc., 2023.
ISSN
0146-4116
eISSN
1558-108X
DOI
10.3103/s0146411623020086
Publisher site
See Article on Publisher Site

Abstract

Image fusion refers to the fusion of images collected by multiple sensors about the same target or scene into one image by image processing technology. In this way, the advantages of multiple sensors can be effectively utilized to obtain more comprehensive feature information of the target or scene, which is conducive to human eye observation and subsequent recognition and processing. The traditional fusion methods are easy to lose the image details, resulting in poor fusion effect. Therefore, this paper proposes a multisource image fusion method based on convolutional sparse coding and a novel fusion strategy. Firstly, the image is decomposed into low-rank and sparse through low-rank decomposition. Then, the sparse part is convoluted and decomposed to obtain a set of sparse filter dictionaries, which are applied to image fusion by convolutional sparse coding. The regional energy-Cauchy fuzzy function rule is adopted for low-rank components. The regional Laplace energy is used for sparse component. Finally, the weighted average method is used to obtain the final fusion result. Experimental results show that the proposed method achieves good results in terms of visual effects and objective evaluation indexes.

Journal

Automatic Control and Computer Sciences – Springer Journals

Published: Apr 1, 2023

Keywords: multisource image fusion; convolutional sparse coding; regional energy-Cauchy fuzzy function; regional Laplace energy

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