Superpixel-based image inpainting with simple user guidance

Abstract

We introduce a new approach for performing image inpainting, by devising an integrative method based on the superpixel segmentation technique and considering minimal user input. Image inpainting methods are concerned with filling in missing or replacing undesired regions in an image. Typically, inpainting methods consider and extrapolate known image data. Superpixels in the immediate neighborhood of the in-painting region are computed and used as source image data to fill in (or replace) the inpainting area. A user provides additional information by specifying line segments in the image to assist the otherwise automatic inpainting process, to ensure that only desirable superpixels are utilized when copying them into the inpainting region. User interaction is minimal, as it is merely necessary to specify a small number of line segments that define image parts to be used as source data in distinct inpainting regions. We provide experimental results demonstrating that our method performs well when compared against other methods, especially concerning the preservation of edges and texture in the inpainted regions.