Low-light image stitching is a technique that enables the stitching of images taken from different perspectives into a large field-of-view image under insufficient lighting conditions. The low contrast and high noise of images caused by inadequate lighting compromise the robustness and quantity of feature extraction, making feature matching and image stitching challenging. In response, this study proposes a low-light image stitching method based on an improved speeded-up robust feature (SURF) algorithm. In this method, a scale space was constructed first using the integral image of low-light images and Laplacian operations were performed, followed by edge extraction and binarization of the images. Further, the edges-in-shaded-region (ESR) image was generated based on the edge-extracted and binarized images to obtain scale weights, thereby dynamically adjusting the SURF feature extraction threshold. This effectively resolves the issue of mismatch between feature point pixel thresholds and overall image brightness, enhancing the robustness of the feature extraction algorithm. Additionally, the obtained scale weights can serve as weighting coefficients for the multiscale Retinex algorithm to achieve better image enhancement effects. In this method, binary descriptors were employed to accelerate the feature description and matching process. Finally, a homography matrix was calculated based on matching relationships to perform homography transformation and stitching of the enhanced images. Experimental results demonstrate that the proposed algorithm effectively improves the speed and performance of low-light image stitching, offering better robustness and adaptability compared with the traditional SURF algorithm.