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In a scenario where multi-modal cameras are operating together, the problemof working with non-aligned images cannot be avoided. Yet, existing imagefusion algorithms rely heavily on strictly registered input image pairs toproduce more precise fusion results, as a way to improve the performance ofdownstream high-level vision tasks. In order to relax this assumption, one canattempt to register images first. However, the existing methods for registeringmultiple modalities have limitations, such as complex structures and relianceon significant semantic information. This paper aims to address the problem ofimage registration and fusion in a single framework, called BusRef. We focus onInfrared-Visible image registration and fusion task (IVRF). In this framework,the input unaligned image pairs will pass through three stages: Coarseregistration, Fine registration and Fusion. It will be shown that the unifiedapproach enables more robust IVRF. We also propose a novel training andevaluation strategy, involving the use of masks to reduce the influence ofnon-reconstructible regions on the loss functions, which greatly improves theaccuracy and robustness of the fusion task. Last but not least, agradient-aware fusion network is designed to preserve the complementaryinformation. The advanced performance of this algorithm is demonstrated by

BusReF: Infrared-Visible images registration and fusion focus on reconstructible area using one set of features