Adaptive segmentation and mask-specific Sobolev inpainting of specular highlights for endoscopic images

Minimally invasive surgical and diagnostic systems rely on endoscopic images of internal organs to assist medical tasks. Specular highlights are common on those images due to the strong reflectivity of the mucus layer on the organs and the relatively high intensity of the light source. This is a significant source of error that can affect the systems' performance. In this paper, we propose a segmentation method of the specular regions based on an automatic color-adaptive threshold and a gradient-based edge detector. The segmented regions are then recovered using a robust mask-specific Sobolev inpainting approach. Experimental results demonstrate the precision and efficiency of the proposed method. In contrast to the existing approaches, the proposed solution does not require manual threshold selection or complex computations to achieve accurate results. Moreover, our method has a real-time performance and can be generalized to various applications.