Object Removal by Depth-guided Inpainting∗

Object removal by image inpainting aims at the visual uniformity of the inpainted blanks among their surroundings. Most inpainting algorithms pursue the structure continuity and texture similarity only in color. In this paper we take the view depth continuity into account and propose a depth-guided inpainting algorithm, in which a single color image and its associated disparity map are inpainted simultaneously. A fast exemplar-based inpainting is applied to fill the blank. Exemplars are randomly selected under depth constraints in initialization and optimized with a nearest neighbor search method in a semi-global way for smooth completion. Experimental results with datasets of different scenes demonstrate the positive impact of depth control in exemplar selection and the efficiency of the proposed algorithm.