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Volume 9 Issue 12
Dec.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2022  >  9(12): 2121-2137
L. F. Tang, Y. X. Deng, Y. Ma, J. Huang, and J. Y. Ma, "SuperFusion: A versatile image registration and fusion network with semantic awareness, " IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp.2021-2137, Dec. 2022. doi: 10.1109/JAS.2022.106082
Citation: L. F. Tang, Y. X. Deng, Y. Ma, J. Huang, and J. Y. Ma, "SuperFusion: A versatile image registration and fusion network with semantic awareness, " IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp.2021-2137, Dec. 2022. doi: 10.1109/JAS.2022.106082
shu

SuperFusion: A Versatile Image Registration and Fusion Network with Semantic Awareness

doi: 10.1109/JAS.2022.106082

  • Electronic Information School, Wuhan University, Wuhan 430072, China

Funds:

the National Natural Science Foundation of China 62276192

the National Natural Science Foundation of China 62075169

the National Natural Science Foundation of China 62061160370

the Key Research and Development Program of Hubei Province 2020BAB113

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    Abstract
  • Image fusion aims to integrate complementary information in source images to synthesize a fused image comprehensively characterizing the imaging scene. However, existing image fusion algorithms are only applicable to strictly aligned source images and cause severe artifacts in the fusion results when input images have slight shifts or deformations. In addition, the fusion results typically only have good visual effect, but neglect the semantic requirements of high-level vision tasks. This study incorporates image registration, image fusion, and semantic requirements of high-level vision tasks into a single framework and proposes a novel image registration and fusion method, named SuperFusion. Specifically, we design a registration network to estimate bidirectional deformation fields to rectify geometric distortions of input images under the supervision of both photometric and end-point constraints. The registration and fusion are combined in a symmetric scheme, in which while mutual promotion can be achieved by optimizing the naive fusion loss, it is further enhanced by the mono-modal consistent constraint on symmetric fusion outputs. In addition, the image fusion network is equipped with the global spatial attention mechanism to achieve adaptive feature integration. Moreover, the semantic constraint based on the pre-trained segmentation model and Lovasz-Softmax loss is deployed to guide the fusion network to focus more on the semantic requirements of high-level vision tasks. Extensive experiments on image registration, image fusion, and semantic segmentation tasks demonstrate the superiority of our SuperFusion compared to the state-of-the-art alternatives. The source code and pre-trained model are publicly available at https://github.com/Linfeng-Tang/SuperFusion.

     

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    • Recommended by Associate Editor Qing-Long Han. (Linfeng Tang and Yuxin Deng contributed equally to this work.)
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