Unsupervised Oral Endoscope Image Stitching Algorithm

Oral endoscope image stitching algorithm is studied to obtain wide-field oral images through regis-tration and stitching,which is of great significance for auxiliary diagnosis.Compared with natural images,oral images have lower textures and fewer features.However,traditional feature-based image stitching methods rely heavily on feature extraction quality,often showing an unsatisfactory performance when stitching images with few features.Moreover,due to the hand-held shooting,there are large depth and perspective disparities between the captured images,which also pose a challenge to image stitching.To overcome the above problems,we propose an unsupervised oral endoscope image stitching algorithm based on the extraction of overlapping regions and the loss of deep features.In the registration stage,we extract the overlapping region of the input images by sketching polygon intersection for feature points screening and estimate homography from coarse to fine on a three-layer feature pyramid structure.Moreover,we calculate loss using deep features instead of pixel values to emphasize the importance of depth disparities in homography estimation.Finally,we reconstruct the stitched images from feature to pixel,which can eliminate artifacts caused by large parallax.Our method is compared with both feature-based and previous deep-based methods on the UDIS-D dataset and our oral endoscopy image dataset.The experimental results show that our algorithm can achieve higher homography estimation accuracy,and better visual quality,and can be effectively applied to oral endoscope image stitching.