Image Inpainting based on Block-based Linear Regression and Minimum Variance Convex Combinations

Katsuya Kohno and Akira Tanaka


Image inpainting, linear regression, EM algorithm, convex combination


Estimation of missing entries in a multivariate data is one of classical problems in the field of statistical science. Linear regression with the EM algorithm is well known as one of popular approaches for this problem. When this approach is applied to block-based image inpainting problems, multiple candidates of estimate for a target lost pixel may be obtained. In our previous work, we proposed a denoising technique for multiple images using the convex combination which minimizes its variance of errors from a true image. In this paper, we propose a novel image inpainting method incorporating the application of the denoising technique to multiple estimates for a target pixel. We also show several results of numerical experiments in order to verify the efficacy of the proposed method.

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