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Rethinking the Image Fusion: A Fast Unified Image Fusion Network based on Proportional Maintenance of Gradient and Intensity

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PMGI_AAAI2020

Code for paper Rethinking the Image Fusion: A Fast Unified Image Fusion Network based on Proportional Maintenance of Gradient and Intensity. This work uses the same network structure and the same form of loss function to realize infrared and visible image fusion, multi-exposure image fusion, medical image fusion, multi-focus image fusion and Pan-sharpening. The all dataset can be found at https://drive.google.com/file/d/1acad2moPxvSVNCyHOeEDO3qbL_KveQOP/view?usp=sharing

If you use the code, please cite the following paper:

Hao Zhang, Han Xu, Yang Xiao, Xiaojie Guo, and Jiayi Ma. "Rethinking the Image Fusion: A Fast Unified Image Fusion Network based on Proportional Maintenance of Gradient and Intensity", in Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), Feb. 2020.

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  • Python 98.0%
  • MATLAB 2.0%