To address the challenges of repetitive and low-texture features in intraoral endoscopic images, a novel methodology for stitching panoramic half jaw images of the oral cavity is proposed. Initially, an enhanced self-attention mechanism guided by Time-Weighting concepts is employed to augment the clustering potential of feature points, thereby increasing the number of matched features. Subsequently, a combination of the Sinkhorn algorithm and Random Sample Consensus (RANSAC) is utilized to maximize the count of matched feature pairs, accurately remove outliers and minimize error. Last, to address the unique spatial alignment among intraoral endoscopic images, a wavelet transform and weighted fusion algorithm based on dental arch arrangement in intraoral endoscopic images have been developed, specifically for use in the fusion stage of intraoral endoscopic images. This enables the local oral images to be precisely positioned along the dental arch, and seamless stitching is achieved through wavelet transformation and a gradual weighted fusion technique. Experimental results demonstrate that this method yields promising outcomes in panoramic stitching tasks for intraoral endoscopic images, achieving a matching accuracy of 84.6% and a recall rate of 78.4% in a dataset with an average overlap of 35%. A novel solution for panoramic stitching of intraoral endoscopic images is provided by this method.
Keywords: RANSAC; attention mechanism; image stitching; intraoral endoscopic imagery; panoramic image generation; weighted Fusion.