Title
A Novel Infrared and Visible Image Information Fusion Method Based on Phase Congruency and Image Entropy
Abstract
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.
Year
DOI
Venue
2019
10.3390/e21121135
ENTROPY
Keywords
Field
DocType
image fusion,image entropy,PCNN,infrared and visible fusion,image decomposition,phase congruency
Mathematical optimization,Pattern recognition,Image fusion,Fusion,Fusion rules,Decomposition method (constraint satisfaction),Artificial intelligence,Artificial neural network,Phase congruency,Entropy (information theory),Mathematics,Contourlet
Journal
Volume
Issue
Citations 
21
12
1
PageRank 
References 
Authors
0.35
0
5
Name
Order
Citations
PageRank
Xinghua Huang110.35
Guanqiu Qi216416.20
Hongyan Wei310.35
Yi Chai410.35
Jaesung Sim520.70