An adaptive image inpainting method based on euler's elastica with adaptive parameters estimation and the discrete gradient method

Abstract

Euler's Elastica is a common approach developed based on minimizing the elastica energy. It is one of the effective approaches to solve the image inpainting problem. Nevertheless, there are two major issues: the Euler's elastica variational image inpainting model itself is multiparameter, and the performance of methods for solving the model is not high. In the article, we propose an adaptive Euler's elastica image inpainting model by combining with adaptive parameter estimation based on the smoothed structure tensor. To implement the model, a numerical algorithm based on the discrete gradient method is developed. The experiments showed that the proposed image inpainting method outperforms other state-of-the-arts methods in terms of inpainted image quality.