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.

中文翻译：

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基于Euler's Elastica的自适应参数估计和离散梯度法的自适应图像修复方法

摘要 Euler 的 Elastica 是一种基于最小化弹性能量的常用方法。它是解决图像修复问题的有效方法之一。然而，存在两个主要问题：Euler的elastica变分图像修复模型本身是多参数的，求解模型的方法性能不高。在文章中，我们结合基于平滑结构张量的自适应参数估计，提出了一种自适应欧拉弹性图像修复模型。为了实现该模型，开发了一种基于离散梯度法的数值算法。实验表明，所提出的图像修复方法在修复图像质量方面优于其他最先进的方法。