It is a challenging task to prevent the staircase effect and simultaneously preserve sharp edges in image inpainting. For this purpose, we present a novel nonconvex extension model that closely incorporates the advantages of total generalized variation and edge-enhancing nonconvex penalties. This improvement contributes to achieve the more natural restoration that exhibits smooth transitions without penalizing fine details. To efficiently seek the optimal solution of the resulting variational model, we develop a fast primal-dual method by combining the iteratively reweighted algorithm. Several experimental results, with respect to visual effects and restoration accuracy, show the excellent image inpainting performance of our proposed strategy over the existing powerful competitors. PDF  XML

中文翻译：

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图像修复的非凸总广义变异模型

防止阶梯效应并同时保留图像修复中的尖锐边缘是一项艰巨的任务。为此，我们提出了一种新颖的非凸扩展模型，该模型紧密结合了总广义变化和边缘增强非凸罚分的优点。这项改进有助于实现更自然的修复，该修复呈现出平滑的过渡效果而不会损害精细的细节。为了有效地找到生成的变分模型的最优解，我们通过结合迭代加权算法来开发一种快速的原始对偶方法。关于视觉效果和恢复精度的几个实验结果表明，我们提出的策略优于现有强大竞争对手的出色图像修复性能。PDF XML