Exploring contour and texture features for context-aware patch-based inpainting

Abstract

In this paper, we explore the use of contour and texture features for context-aware patch-based image inpainting. Both of these features are obtained by analysing the image filtered with the bank of filters at multiple orientations and scales, specifically Gabor filters. We use contour features to define a novel patch priority, which represents the main contribution of this paper. The priority is used to determine the filling order of the missing region, which is crucial for the success of the algorithm. Our goal is to make better differentiation between patches with structured, textured and smooth content than related definitions. We employ this novel priority within our recently proposed context-aware inpainting method, which uses contextual descriptors to find contextually similar image regions to which the search for well matching replacement patches is constrained. Here we use texture features, together with color features, as contextual descriptors of image regions. The benefit of the context-aware approach is twofold: the chance of choosing wrong matches is reduced and the search for candidate patches is accelerated. Experimental results demonstrate the benefit of the proposed method compared to state-of-the-art.