CMEFusion: Cross-Modal Enhancement and Fusion of FIR and Visible Images

Abstract

The fusion of far infrared (FIR) and visible images aims to generate a high-quality composite image that contains salient structures and abundant texture details for human visual perception. However, the existing fusion methods typically fall short of utilizing complementary source image characteristics to boost the features extracted from degraded visible or FIR images, thus they cannot generate satisfactory fusion results in adverse lighting or weather conditions. In this paper, we propose a novel Cross-Modal multispectral image Enhancement and Fusion framework (CMEFusion), which adaptively enhances both FIR and visible inputs by leveraging complementary cross-modal features to further facilitate multispectral feature aggregation. Specifically, we first present a new cross-modal image enhancement sub-network (CMIENet), which is built on a CNN-Transformer hybrid architecture to perform the complementary exchange of local-salient and global-contextual features extracted from FIR and visible modalities, respectively. Then, we design a gradient-content differential fusion sub-network (GCDFNet) to progressively integrate decoupled gradient and content information via modified central difference convolution. Finally, we present a comprehensive joint enhancement-fusion multi-term loss function to drive the model to narrow the optimization gap between the above-mentioned two sub-networks based on the self-supervised aspects of exposure, color, structure, and intensity. In this manner, the proposed CMEFusion model facilitates better-performing visible and FIR image fusion in an end-to-end way, achieving enhanced visual quality with more natural and realistic appearances. Extensive experiments validate that CMEFusion surpasses state-of-the-art image fusion algorithms, as evidenced by superior performance in both visual quality and quantitative evaluations.