Abstract
Infrared and visual image fusion aims to obtain a complex image which contains more recognizable information. To obtain the complex image, a fusion algorithm via multi-modal decomposition and pulse-coupled neural network (PCNN) in gradient domain fusion measure is proposed. Firstly, the source images are decomposed into three layers through the decomposition model. Then, a gradient domain PCNN fusion measure is employed in the three layers. Finally, the fused image is reconstructed through the three fused layers. Experimental results demonstrate that the proposed algorithm performs effectively in both qualitative and quantitative measures.
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Acknowledgement
The authors are grateful to the anonymous reviews for their valuable comments and suggestion. This paper is supported by National Natural Science Foundation of China (61675160); 111 Project (B17035), China Scholarship Council (CSC201906960047). We thank TNO providing their dataset freely [22].
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Tan, W., Zhang, J., Qian, K., Du, J., Xiang, P., Zhou, H. (2020). Infrared and Visual Image Fusion via Multi-modal Decomposition and PCNN in Gradient Domain Fusion Measure. In: McDaniel, T., Berretti, S., Curcio, I., Basu, A. (eds) Smart Multimedia. ICSM 2019. Lecture Notes in Computer Science(), vol 12015. Springer, Cham. https://doi.org/10.1007/978-3-030-54407-2_27
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