Texture Oriented Image Inpainting Based on Local Statistical Model
نویسنده
چکیده
Image inpainting as a means of substituting missing image parts can become difficult when the image is textured. In this paper we apply a local statistical model of the source color image with the aim to predict missing texture regions. We have shown in a series of papers that textures can be modeled locally by estimating the joint probability density of spectral pixel values in a suitably chosen observation window. For the sake of image inpainting we estimate the joint multivariate density in the form of a Gaussian mixture of product components. The missing image region is inpainted iteratively by step-wise prediction of the unknown spectral values.
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