Abstract
Infrared and visible image fusion can produce a composite image which has high contrast and rich background details of the scene. In view of the defects of some existing infrared and visible fusion method, such as low contrast and unclear background details, we propose a novel multi-scale fusion method based on the combination of non-sampled contourlet transform (NSCT), sparse representation and pulse coupled neural network. In our method, the source images are firstly decomposed into one low frequency sub-band and high frequency sub-bands at different scales and directions using NSCT. Fusion rules based on the sparse representation and modified PCNN are developed, and then used for fusion of the low sub-band and high frequency sub-bands, respectively. In the modified PCNN developed in this paper, we use Sum-Modified-Laplacian and Log-Gabor energy as values of the linking strength instead of setting it a constant. Each of the linking strength corresponds to an ignition map, the average of the two results is taken as the final PCNN output. The fused image are finally obtained by performing the inverse NSCT. Comparison experiment results show that the fused image produced by the proposed method has high contrast and rich details, as well as the greatly improved objective evaluation indexes values.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 61571046, No. 61272026, 61571046, No. 61370193), Science and Technology Development Fund of Macao SAR (No. 097/2013/A3).
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Wang, X., Yao, L., Song, R., Xie, H. (2017). A New Infrared and Visible Image Fusion Algorithm in NSCT Domain. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_39
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DOI: https://doi.org/10.1007/978-3-319-63309-1_39
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