The application of machine vision in object identification and classification has significantly enhanced recognition efficiency. Nevertheless, for non-ferrous scrap metals with poor surface smoothness, the unevenness of reflected light results in the generation of dark regions in the images, obscuring a considerable amount of detailed information and reducing the recognition accuracy. Addressing these challenges, we propose a method for enhancing the details of dark regions based on the RGB-NIR image fusion theory, integrating detailed information from NIR images into RGB images. First, a robust deep residual denoising network is constructed to estimate and remove noise in images. Subsequently, to address the difficulty of extracting structural features in dark regions, a multi-scale spatial deep structure feature extraction module based on channel attention blocks is developed. This module effectively extracts the structural features of RGB and NIR image pairs, with the target image serving as the supervisory signal. Finally, guided by the theory of structural inconsistency, multi-scale feature maps are fused. The image fusion network adopts an encoder-decoder architecture embedded with residual channel attention blocks. The experimental results indicate that the approach proposed in this study demonstrates notable efficacy in image denoising and detail enhancement.