## 1 Introduction  
With advancements in sensor technology, acquired images now feature high information content, resolution, and multi-source diversity. However, a single processing method often proves inadequate for synthesizing multi-modal information effectively. Multi-modal image fusion addresses this challenge by integrating data from multiple sensors into a single composite image that combines their distinct characteristics, thereby providing a more comprehensive scene representation [1]. Among fusion techniques, infrared and visible image fusion is the most widely applied. Visible images provide high spatial resolution and detailed scenes but are affected by adverse conditions, like nighttime, fog, or obstacles [2, 3]. Infrared sensors capture thermal radiation, enabling clear target-background differentiation. Fusing these modalities preserves thermal targets while retaining rich background details [4, 5], making it crucial in military reconnaissance, target tracking, security monitoring, and remote sensing [6]. Researchers have developed numerous fusion methods for multi-modal image processing, which can be broadly categorized into traditional approaches and deep learning-based techniques [2, 7]. Traditional methods encompass multi-scale decomposition (MSD) [8, 9, 10],sparse representation [11; 12], saliency-based approaches [13; 14], and subspace techniques [15; 16]. Deep learning-based methods leverage various architectures, including CNNs, auto-encoders, GANs, and transformers [17; 18; 19; 20]. However, most existing methods primarily address normal lighting conditions, often neglecting the challenges posed by nighttime illumination degradation. While infrared data provides critical thermal information, excessive reliance on it may result in the loss of valuable scene details, ultimately undermining the fundamental objective of complementary information integration [2]. To overcome these limitations, we present VCAFusion, an innovative infrared-visible fusion framework designed to enhance visual perception while addressing illumination degradation. Our methodology integrates the following three key technical contributions: (1) an adaptive brightness adjustment model based on the retinex theory and multi-scale principles that effectively enhances weak details in visible images; (2) a biologically-inspired adaptive enhancement function that optimizes texture refinement while maintaining edge sharpness; and (3) a multi-scale decomposition model employing side window filtering [21] for precise scale separation and robust feature extraction. Furthermore, we incorporate a saliency-preserving strategy that effectively maintains critical information across different scales, thereby ensuring enhanced clarity and structural integrity in the fused output. While existing methods have advanced multi-modal fusion, significant gaps remain in handling nighttime conditions. Table 1 further summarizes the advantages and disadvantages of different fusion methods. As shown in Figure 1, VCAFusion outperforms some state-of-the-art methods, like MSID [10] and SeAFusion [17], delivering richer visual features. It highlights high-temperature targets, enhances subtle textures, and seamlessly integrates essential information. The red and green regions in the comparison further illustrate these improvements. In summary, this paper makes the following main contributions:  
* An infrared-visible fusion method enhancing detail preservation and visual quality in low-light conditions.