Seam-Cutting Image Stitching Method Based on DM-Net and SU-Net

Traditional image stitching methods tend to rely on complex and salient geometric features to achieve better stitching results. However, these features are only effective in specific natural scenes with sufficient geometric structures. In contrast, deep learning-based stitching approaches overcome adverse conditions by adaptively learning robust semantic features. Nevertheless, in scenarios with low or highly complex feature content, these methods may fail due to insufficient feature extraction, leading to errors. Moreover, conventional seam-finding methods do not consider the optimal positioning of seams during the stitching process, which can result in misalignment caused by poor seam placement. To address these issues, we propose a novel unsupervised feature extraction method(DM-Net) combined with an optimal seam selection strategy(SU-Net) for image stitching. Our approach leverages a time-series algorithm to effectively fuse local and global features, thereby enhancing the correlation between them and ensuring a global perspective of the feature relationships. During stitching, the optimal seam is determined by sharing features between image pairs. Experimental results demonstrate that the proposed method outperforms other seam-cutting stitching techniques, yielding superior results.