“A research team has proposed an innovative multi-scale structured fusion method to address the challenges faced by infrared and visible light image fusion in low light environments. This method first utilizes dynamic range compression algorithm to enhance visible light images under low light conditions, and then decomposes the enhanced image and infrared image into low-frequency and high-frequency information through multi-scale structured decomposition. In the fusion process, the research team innovatively used root mean square error coefficient to fuse low-frequency information, and used information entropy adaptive weight adjustment strategy to perform secondary optimization on high-frequency information. Finally, the region pixel enhancement algorithm based on grayscale classification is used to improve the contrast of the fused image. The experimental results show that this method performs well on the TNO and CVC-14 datasets, not only achieving better objective evaluation results in indicators such as average gradient, cross entropy, edge strength, standard deviation, and spatial frequency, but also significantly outperforming other comparative methods in terms of overall visual effects. This groundbreaking research achievement not only enriches the details, textures, and clarity of image fusion technology, but also significantly shortens the fusion time, providing a new solution for image fusion in low light environments.”