Feature dynamic alignment and refinement for infrared–visible image fusion: Translation robust fusion

Translational displacement between source images from different sensors is a general phenomenon, which will cause performance degradation on image fusion. To tackle this issue, a straightforward way is to make source images registration first. However, due to the large modality-gap between the infrared image and the visible image, it is too challenging to achieve completely registered images. In this paper, a novel registration-free fusion method is primarily proposed for infrared and visible images with translational displacement, which transforms the problem of image registration to feature alignment in an end-to-end framework. Specifically, we propose a cross-modulation strategy followed by feature dynamic alignment, so that the spatial correlation of shifts is adaptively measured and the aligned features can be dynamically extracted. A feature refinement module is additionally designed based on the local similarity, which enhances the textures related information while suppresses artifacts related information. Thanks to these strategies, our experimental results on infrared–visible images with translational displacement achieve dramatic enhancement compared with state-of-the-arts. To the best of our knowledge, this is the first work on infrared–visible image fusion without strict registration. It does break the constraint of existing image-registration based two-step strategies and provide a simple but efficient way for multi-modal image fusion. The source code will be released at https://github.com/lhf12278/RFVIF.