Adaptive specular reflection detection and inpainting in colonoscopy video frames
This work addresses image quality enhancement for colonoscopy diagnosis, but it is incremental as it builds on existing detection and inpainting techniques.
The paper tackled the problem of specular reflections in colonoscopy video frames by proposing a two-phase detection and inpainting method, achieving an accuracy of 99.68% and a Dice score of 71.79% on a colonoscopy image database.
Colonoscopy video frames might be contaminated by bright spots with unsaturated values known as specular reflection. Detection and removal of such reflections could enhance the quality of colonoscopy images and facilitate diagnosis procedure. In this paper we propose a novel two-phase method for this purpose, consisting of detection and removal phases. In the detection phase, we employ both HSV and RGB color space information for segmentation of specular reflections. We first train a non-linear SVM for selecting a color space based on image statistical features extracted from each channel of the color spaces. Then, a cost function for detection of specular reflections is introduced. In the removal phase, we propose a two-step inpainting method which consists of appropriate replacement patch selection and removal of the blockiness effects. The proposed method is evaluated by testing on an available colonoscopy image database where accuracy and Dice score of 99.68% and 71.79% are achieved respectively.