Under low-light conditions, the fusion of infrared and visible images often results in images with poor contrast, lacking in detail, and requiring a lengthy processing time. To address these issues, this paper introduces an enhanced multi-scale structural fusion approach. Initially, it improves the low-light visible image using a dynamic range compression enhancement algorithm. Subsequently, through a multi-scale structural image decomposition method, it separates the enhanced visible and infrared images into their low-frequency and high-frequency components. For image fusion, the low-frequency components of both image types are merged using a technique based on the root mean square error coefficient. The high-frequency components are initially fused in a straightforward manner, followed by an optimized fusion using a self-adaptive weight adjustment based on image information entropy. Afterward, by reversing the multi-scale structural decomposition, the fused low and high-frequency components are combined to form a complete image. To further enhance the image contrast, a regional pixel enhancement algorithm based on gray level classification is introduced. The effectiveness of this method is compared with nine conventional infrared and visible image fusion techniques, both qualitatively and quantitatively, using TNO and CVC-14 datasets. The proposed method demonstrates superior performance in terms of average gradient, cross entropy, edge intensity, standard deviation, and spatial frequency, along with an improved overall visual quality. This confirms that the images produced by the proposed method exhibit enhanced detail, clarity, contrast, and are processed more quickly.