Automatic Structure-Aware Inpainting for Complex Image Content

A fully automatic algorithm for substitution of missing visual information is presented. The missing parts of a picture may have been caused by damages to or transmission loss of the physical picture. In the former case, the picture is scanned and the damage is considered as holes in the picture while, in the latter case, the lost areas are identified. The task is to derive subjectively matching contents to be filled into the missing parts using the available picture information. The proposed method arises from the observation that dominant structures, such as object contours, are important for human perception. Hence, they are accounted for in the filling process by using tensor voting, which is an approach based on the Gestalt laws of proximity and good continuation. Missing textures surrounding dominant structures are determined to maximize a new segmentation-based plausibility criterion. An efficient post-processing step based on a cloning method minimizes the annoyance probability of the inpainted textures given a boundary condition. The experiments presented in this paper show that the proposed method yields better results than the state-of-the-art.