CHFusion: A Cross-modality High-resolution Representation Framework for Infrared and Visible Image Fusion

In our study, we proposed a novel infrared and visible image fusion framework based on cross-modality transfer and high-resolution representation, termed as CHFusion. On the one hand, the high-resolution representation backbone is devised to receive multi-scale information and maintain high-resolution representation. More specifically, the proposed method involves the pyramid cross-modality feature transfer module to achieve information interaction with different modalities and resolutions. In particular, we introduce the gradient block to obtain texture information of the source image and then supplement it with high-resolution features. We utilize the adaptive channel attention block to compress high-resolution features and then guide image reconstruction. Moreover, a cross-modality high-resolution aggregation block is used to integrate multi-scale information. On the other hand, we propose a difference-aware algorithm, which can generate a pair of weights and then use the weights to construct the difference-aware loss, and then difference-aware loss, a texture loss, and an intensity loss to guide our network to preserve abundant texture information and optimally salient target. Extensive experiments demonstrate the superiority of our method over state-of-the-art alternatives in terms of object maintenance and texture preservation.