论文标题
快速的随机插件ADMM，用于成像逆问题
A Fast Stochastic Plug-and-Play ADMM for Imaging Inverse Problems
论文作者
论文摘要
在这项工作中，我们提出了一种有效的随机插入式播放（PNP）算法，用于成像逆问题。最近已经提出了PNP随机梯度下降方法，并显示出与标准确定性PNP方法相比，在某些成像应用中的性能提高了。但是，当前的随机PNP方法需要经常计算出可能在计算上很昂贵的图像deoisiser。为了克服这一限制，我们提出了一种新的随机PNP-ADMM方法，该方法基于在不精确的ADMM框架内引入随机梯度下降的内环。我们根据标准假设为我们的算法的定点收敛提供了理论保证。我们的数值结果证明了与最先进的PNP方法相比，我们的方法的有效性。
In this work we propose an efficient stochastic plug-and-play (PnP) algorithm for imaging inverse problems. The PnP stochastic gradient descent methods have been recently proposed and shown improved performance in some imaging applications over standard deterministic PnP methods. However, current stochastic PnP methods need to frequently compute the image denoisers which can be computationally expensive. To overcome this limitation, we propose a new stochastic PnP-ADMM method which is based on introducing stochastic gradient descent inner-loops within an inexact ADMM framework. We provide the theoretical guarantee on the fixed-point convergence for our algorithm under standard assumptions. Our numerical results demonstrate the effectiveness of our approach compared with state-of-the-art PnP methods.
