Journal
ENTROPY
Volume 21, Issue 12, Pages -Publisher
MDPI
DOI: 10.3390/e21121135
Keywords
image fusion; image entropy; PCNN; infrared and visible fusion; image decomposition; phase congruency
Categories
Funding
- National Natural Science Foundation of China [61906026]
- Common Key Technology Innovation Special of Key Industries of Chongqing science and Technology Commission [cstc2017zdcy-zdyfX0067, cstc2017zdcy-zdyfX0055]
- Artificial Intelligence Technology Innovation Significant Theme Special Project of Chongqing Science and Technology Commission [cstc2017rgzn-zdyfX0014, cstc2017rgzn-zdyfX0035]
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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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