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Colonoscopy video frames might be contaminated by bright spots withunsaturated values known as specular reflection. Detection and removal of suchreflections could enhance the quality of colonoscopy images and facilitatediagnosis procedure. In this paper we propose a novel two-phase method for thispurpose, consisting of detection and removal phases. In the detection phase, weemploy both HSV and RGB color space information for segmentation of specularreflections. We first train a non-linear SVM for selecting a color space basedon image statistical features extracted from each channel of the color spaces.Then, a cost function for detection of specular reflections is introduced. Inthe removal phase, we propose a two-step inpainting method which consists ofappropriate replacement patch selection and removal of the blockiness effects.The proposed method is evaluated by testing on an available colonoscopy imagedatabase where accuracy and Dice score of 99.68% and 71.79% are achievedrespectively.

Adaptive specular reflection detection and inpainting in colonoscopy video frames