Image extrapolation for photo stitching using nonlocal patch-based inpainting

Abstract

Image alignment and mosaicing are usually performed on a set of overlapping images, using features in the area of overlap for seamless stitching. In many cases such images have different size and shape. So we need to crop panoramas or to use image extrapolation for them. This paper focuses on novel image inpainting method based on modified exemplar-based technique. The basic idea is to find an example (patch) from an image using local binary patterns, and replacing non-existed ('lost') data with it. We propose to use multiple criteria for a patch similarity search since often in practice existed exemplar-based methods produce unsatisfactory results. The criteria for searching the best matching uses several terms, including Euclidean metric for pixel brightness and Chi-squared histogram matching distance for local binary patterns. A combined use of textural geometric characteristics together with color information allows to get more informative description of the patches. In particular, we show how to apply this strategy for image extrapolation for photo stitching. Several examples considered in this paper show the effectiveness of the proposed approach on several test images.