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Exemplar-Based Image Inpainting using an Affine Invariant Similarity Measure

Abstract

Patch-based approaches are used in state-of-the-art methods for image inpainting. This paper presents a new method for exemplar-based image inpainting using transformed patches. The transformation is determined for each patch in a fully automatic way from a surrounding texture content. We build upon a recent affine invariant patch similarity measure that performs an appropriate patch comparison by automatically adapting the size and shape of the patches. As a consequence, it intrinsically extends the set of available source patches to copy information from. We incorporate this measure into a variational formulation for inpainting and present a numerical algorithm for optimizing it. We show that our method can be applied to complete a perspectively distorted texture as well as to automatically inpaint one view of a scene using other view of the same scene as a source. We present experimental results both for gray and color images, and a comparison with some exemplar-based image inpainting methods.
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Dates and versions

hal-01829625 , version 1 (04-07-2018)

Identifiers

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Vadim Fedorov, Pablo Arias, Gabriele Facciolo, Coloma Ballester. Exemplar-Based Image Inpainting using an Affine Invariant Similarity Measure. Computer Vision, Imaging and Computer Graphics Theory and Applications 11th International Joint Conference, VISIGRAPP 2016, Rome, Italy, February 27 – 29, 2016, Revised Selected Papers , 2017, Communications in Computer and Information Science book series, ⟨10.1007/978-3-319-64870-5_22⟩. ⟨hal-01829625⟩
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