A Novel Infrared and Visible Image Information Fusion Method Based on Phase Congruency and Image Entropy
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Key Laboratory of Complex System Safety and Control, Ministry of Education, Chongqing University, Chongqing 400044, China
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Computer Information Systems Department, Buffalo State College, Buffalo, NY 14222, USA
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College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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Department of Mathematics and Computer Information Science, Mansfield University of Pennsylvania, Mansfield, PA 16933, USA
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(12), 1135; https://doi.org/10.3390/e21121135
Received: 17 October 2019
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Revised: 18 November 2019
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Accepted: 19 November 2019
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Published: 21 November 2019
(This article belongs to the Special Issue Entropy-Based Algorithms for Signal Processing)
In multi-modality image fusion, source image decomposition, such as multi-scale transform (MST), is a necessary step and also widely used. However, when MST is directly used to decompose source images into high- and low-frequency components, the corresponding decomposed components are not precise enough for the following infrared-visible fusion operations. This paper proposes a non-subsampled contourlet transform (NSCT) based decomposition method for image fusion, by which source images are decomposed to obtain corresponding high- and low-frequency sub-bands. Unlike MST, the obtained high-frequency sub-bands have different decomposition layers, and each layer contains different information. In order to obtain a more informative fused high-frequency component, maximum absolute value and pulse coupled neural network (PCNN) fusion rules are applied to different sub-bands of high-frequency components. Activity measures, such as phase congruency (PC), local measure of sharpness change (LSCM), and local signal strength (LSS), are designed to enhance the detailed features of fused low-frequency components. The fused high- and low-frequency components are integrated to form a fused image. The experiment results show that the fused images obtained by the proposed method achieve good performance in clarity, contrast, and image information entropy.
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Keywords:
image fusion; image entropy; PCNN; infrared and visible fusion; image decomposition; phase congruency
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MDPI and ACS Style
Huang, X.; Qi, G.; Wei, H.; Chai, Y.; Sim, J. A Novel Infrared and Visible Image Information Fusion Method Based on Phase Congruency and Image Entropy. Entropy 2019, 21, 1135. https://doi.org/10.3390/e21121135
AMA Style
Huang X, Qi G, Wei H, Chai Y, Sim J. A Novel Infrared and Visible Image Information Fusion Method Based on Phase Congruency and Image Entropy. Entropy. 2019; 21(12):1135. https://doi.org/10.3390/e21121135
Chicago/Turabian StyleHuang, Xinghua, Guanqiu Qi, Hongyan Wei, Yi Chai, and Jaesung Sim. 2019. "A Novel Infrared and Visible Image Information Fusion Method Based on Phase Congruency and Image Entropy" Entropy 21, no. 12: 1135. https://doi.org/10.3390/e21121135
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