  The salient information of an infrared image and the abundant texture of a
visible image can be fused to obtain a comprehensive image. As can be known,
the current fusion methods based on Transformer techniques for infrared and
visible (IV) images have exhibited promising performance. However, the
attention mechanism of the previous Transformer-based methods was prone to
extract common information from source images without considering the
discrepancy information, which limited fusion performance. In this paper, by
reevaluating the cross-attention mechanism, we propose an alternate Transformer
fusion network (ATFuse) to fuse IV images. Our ATFuse consists of one
discrepancy information injection module (DIIM) and two alternate common
information injection modules (ACIIM). The DIIM is designed by modifying the
vanilla cross-attention mechanism, which can promote the extraction of the
discrepancy information of the source images. Meanwhile, the ACIIM is devised
by alternately using the vanilla cross-attention mechanism, which can fully
mine common information and integrate long dependencies. Moreover, the
successful training of ATFuse is facilitated by a proposed segmented pixel loss
function, which provides a good trade-off for texture detail and salient
structure preservation. The qualitative and quantitative results on public
datasets indicate our ATFFuse is effective and superior compared to other
state-of-the-art methods.
