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Open AccessArticle

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
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Author to whom correspondence should be addressed.
Entropy 2019, 21(12), 1135; https://doi.org/10.3390/e21121135
Received: 17 October 2019 / Revised: 18 November 2019 / Accepted: 19 November 2019 / 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. View Full-Text
Keywords: image fusion; image entropy; PCNN; infrared and visible fusion; image decomposition; phase congruency image fusion; image entropy; PCNN; infrared and visible fusion; image decomposition; phase congruency
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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.

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