Fast image completion method using patch offset statistics

Image completion is a technique that involves repairing damaged images or filling in missing regions in a visually pleasing manner. In this work, a novel fast image completion approach is proposed, which finds the best textures to be filled into the unknown areas through internal statistics of natural images. Firstly, we create statistics from spatial offsets (relative positions) of similar patches in the image. From these statistics, a sparse representation of the most frequent offsets is derived. These offsets are then used to fill the missing region by combining a stack of shifted images using a weighted means method. Hereby, the offsets are considered in descending order in the filling routine according to their importance. Experimental results show that the proposed method yields subjective improvements compared to the state-of-the-art. Furthermore, the method synthesizes missing regions faster than existing methods.