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

作     者：Linfeng Tang Yuxin Deng Yong Ma Jun Huang Jiayi Ma Linfeng Tang;Yuxin Deng;Yong Ma;Jun Huang;Jiayi Ma

作者机构：Electronic Information SchoolWuhan UniversityWuhan 430072China IEEE 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2022年第9卷第12期

页      面：2121-2137页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by the National Natural Science Foundation of China(62276192,62075169,62061160370) the Key Research and Development Program of Hubei Province(2020BAB113) 

主　　题：Global spatial attention image fusion image registration mutual promotion semantic awareness 

摘      要：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 *** 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 Super Fusion. 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 Super Fusion compared to the state-of-the-art *** source code and pre-trained model are publicly available at https://***/Linfeng-Tang/Super Fusion.