Code of U2Fusion: a unified unsupervised image fusion network for multiple image fusion tasks, including multi-modal, multi-exposure and multi-focus image fusion.

Overview

U2Fusion

Code of U2Fusion: a unified unsupervised image fusion network for multiple image fusion tasks, including multi-modal (VIS-IR, medical), multi-exposure and multi-focus image fusion.

Tips:

Large files should be downloaded separately, including the following files:

For training:

If this work is helpful to you, please cite it as:

@article{xu2020u2fusion,
  title={U2Fusion: A Unified Unsupervised Image Fusion Network},
  author={Xu, Han and Ma, Jiayi and Jiang, Junjun and Guo, Xiaojie and Ling, Haibin},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2020},
  publisher={IEEE}
}

If you have any question, please email to me ([email protected]).

Issues
  • Are the medical datasets used when training on task1?

    Are the medical datasets used when training on task1?

    hi, thank you for sharing your code. I have some questions for you.

    are the medical dataset used when training on task1? It seems that the datasets you provide did not include the medical dataset, and the dataset did not seems to be used in your code. if used, could you please provide a link to download it and update the code to use it? looking forward to an early reply, thank you.

    opened by L-D-Luffy 0
  • about output image bit width

    about output image bit width

    Hi, Han Xu

    Thank you for your excellent work!

    In this work, is the image after the fusion of the two LDR images still 8bit? 12bit or higher.

    Thank you!

    opened by Qirui-Y 1
  • Fusing Multiple Images

    Fusing Multiple Images

    Thanks for the nice work! I have a question regarding fusing multiple images. The paper mentions fusing multiple images sequentially (two at a time.)

    But it appears to me that the loss used for training can be easiliy generalized to more than two images and hence achieving multiple-image (more than two images) fusion as one single pass to the DenseNet. Am I right or do I miss something?

    Thanks!

    opened by macrohuang1993 0
Owner
Han Xu
Han Xu
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