论文标题
低等级的先验和L0规范以消除图像中的脉冲噪声
Low rank prior and l0 norm to remove impulse noise in images
论文作者
论文摘要
基于补丁的低级是图像处理的重要先验假设。此外，根据我们的计算，L0规范的优化对应于随机价值脉冲噪声下的最大似然估计。因此，在本文中，我们结合了确切的等级和L0规范来消除噪声。它使用乘数的交替方向方法（ADMM）正式解决，我们以前的基于贴片的加权滤波器（PWMF）产生初始图像。由于该模型不是凸，因此我们将其视为插件ADMM，并且不讨论理论收敛属性。实验表明，该方法的性能非常好，尤其是对于弱或中造影剂图像。
Patch-based low rank is an important prior assumption for image processing. Moreover, according to our calculation, the optimization of l0 norm corresponds to the maximum likelihood estimation under random-valued impulse noise. In this article, we thus combine exact rank and l0 norm for removing the noise. It is solved formally using the alternating direction method of multipliers (ADMM), with our previous patch-based weighted filter (PWMF) producing initial images. Since this model is not convex, we consider it as a Plug-and-Play ADMM, and do not discuss theoretical convergence properties. Experiments show that this method has very good performance, especially for weak or medium contrast images.
