The blind image inpainting problem need to be handle when faced with a large number of images, especially medical images in medical health. For the proposed nonconvex sparse optimization model, a proximal based alternating direction method of multipliers (PADMM) method is designed to solve the problem. Firstly, sparse regularization is imposed to the binary mask since the missing pixels are sparse in our experiments. Secondly, the total variation term is utilized to describe the underlying clean image. Finally, regularization of the fidelity term is used to solve the given blind inpainting problem. Experiments show that this method has better performance than traditional method, and could deal with the blind image inpainting problem.

中文翻译：

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使用混合噪声和完全正则化的盲图像修复


面对大量图像，特别是医疗健康领域的医学图像，需要处理盲图像修复问题。对于所提出的非凸稀疏优化模型，设计了一种基于近端的交替方向乘子法（PADMM）来解决该问题。首先，由于在我们的实验中丢失的像素是稀疏的，因此对二元掩模进行稀疏正则化。其次，利用总变分项来描述底层的干净图像。最后，使用保真度项的正则化来解决给定的盲修复问题。实验表明，该方法比传统方法具有更好的性能，可以解决图像盲修复问题。