Abstract
To compensate for the problems that arise during infrared and visible image fusion, such as lack of detailed information, ringing, incomplete scene information, low contrast, and “virtual shadow”. Based on the orthogonal discrete Q-shift dual-tree filter, an image fusion method combined with morphological image enhancement and dual-tree complex wavelet is proposed. Firstly, the morphological opening and closing operations are used to enhance the source image. Secondly, the enhanced image is decomposed into high-low frequency subbands by the dual-tree complex wavelet filter, and the low frequency subbands adopt a local mean fusion method according to the degree of correlation. The high frequency subbands image adopt the fusion principle of absolute maximum; finally, fusion image obtained by reconstruction. Comparing the experimental results, the proposed method significantly improves the image fusion quality indexes such as average gradient, information entropy, spatial frequency and standard deviation.
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Li, C., Lei, L., Zhang, X. (2020). Infrared and Visible Image Fusion Based on Morphological Image Enhancement of Dual-Tree Complex Wavelet. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_80
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DOI: https://doi.org/10.1007/978-3-030-32591-6_80
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