Conference Paper

Image Inpainting Considering Brightness Change and Spatial Locality of Textures and Its Evaluation

DOI: 10.1007/978-3-540-92957-4_24 Conference: Advances in Image and Video Technology, Third Pacific Rim Symposium, PSIVT 2009, Tokyo, Japan, January 13-16, 2009. Proceedings
Source: DBLP


Image inpainting techniques have been widely investigated to remove undesired objects in an image. Conventionally, missing
parts in an image are completed by optimizing the objective function using pattern similarity. However, unnatural textures
are easily generated due to two factors: (1) available samples in the image are quite limited, and (2) pattern similarity
is one of the required conditions but is not sufficient for reproducing natural textures. In this paper, we propose a new
energy function based on the pattern similarity considering brightness changes of sample textures (for (1)) and introducing
spatial locality as an additional constraint (for (2)). The effectiveness of the proposed method is successfully demonstrated
by qualitative and quantitative evaluation. Furthermore, the evaluation methods used in much inpainting research are discussed.

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    • "This formula gives an explicit comparison and assumes that the image domain is the Euclidean plane. It generalizes approaches to patch comparison applied, for example, in [8] [20] [29] [13]. The purpose in [3] was to define such measures of similarity in the case of images defined on Riemannian manifolds (e.g., the image plane endowed with an anisotropic metric, like the structure tensor). "
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    ABSTRACT: In this paper we study the problem of comparing two patches of images defined on Riemannian manifolds which in turn can be defined by each image domain with a suitable metric depending on the image. For that we single out one particular instance of a set of models defining image similarities that was earlier studied in [C. Ballester et al., Multiscale Model. Simul., 12 (2014), pp. 616--649], using an axiomatic approach that extended the classical Álvarez--Guichard--Lions--Morel work to the nonlocal case. Namely, we study a linear model to compare patches defined on two images in $\mathbb{R}^N$ endowed with some metric. Besides its genericity, this linear model is selected by its computational feasibility since it can be approximated leading to an algorithm that has the complexity of the usual patch comparison using a weighted Euclidean distance. Moreover, we propose and study some intrinsic metrics which we define in terms of affine covariant structure tensors and we discuss their properties. These tensors are defined for any point in the image and are intrinsically endowed with affine covariant neighborhoods. We also discuss the effect of discretization over the affine covariance properties of the tensors. We illustrate our theoretical results with numerical experiments.
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    • "Wexler et al. (2007) formulate the reconstruction procedure as an optimization problem which employs a combination of dynamic space–time and tree structures. Kawai et al. (2009) rely on modifications of the energy functional proposed in Wexler et al. (2007) improving the spatial localization of the similarity weights and brightness invariance. Komodakis and Tziritas (2007) employ variations of the sum-product algorithm (loopy belief propagation) for graphs with cycles associated to ''prioritybased message scheduling'' during the filling process. "
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    ABSTRACT: In this work we propose a new image inpainting technique that combines texture synthesis, anisotropic diffusion, transport equation and a new sampling mechanism designed to alleviate the computational burden of the inpainting process. Given an image to be inpainted, anisotropic diffusion is initially applied to generate a cartoon image. A block-based inpainting approach is then applied so that to combine the cartoon image and a measure based on transport equation that dictates the priority on which pixels are filled. A sampling region is then defined dynamically so as to hold the propagation of the edges towards image structures while avoiding unnecessary searches during the completion process. Finally, a cartoon-based metric is computed to measure likeness between target and candidate blocks. Experimental results and comparisons against existing techniques attest the good performance and flexibility of our technique when dealing with real and synthetic images.
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    • "Ces algorithmes génèrent un patchwork de morceaux d'images, en les choisissant itérativement comme les plus similaires à ceux présents à la frontière du domaine d'inpainting . Ces méthodes donnent des résultats très intéressants en terme de reconstruction de larges zones texturées.Des variantes proposent de mélanger ces deux approches pour mettre en oeuvre des méthodes hybrides[1,7,12], d'autres[8,14]de moyenner plusieurs patchs candidats avant de les recoller . Dans tous les cas, il existe toujours des configurations locales qui ne permettent pas de sélectionner des patchs satisfaisants au critère de similarité choisi. "

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