论文标题
基于总变化的相位检索的凸增强
Convex Augmentation for Total Variation Based Phase Retrieval
论文作者
论文摘要
阶段检索是重要的物理和工业应用的重要问题。在本文中，我们考虑了下层信号测量的大小被高斯噪声损坏的情况。我们引入了基于总变异正则化的相位检索凸的扩增方法。与流行的凸松弛模型（如Phaselift）相反，我们的模型可以通过修改的半耐乘积方向方法（SPADMM）有效地解决。修改后的SPADMM比标准的SPADMM更一般，更灵活，并且本文还建立了其收敛性。进行了广泛的数值实验，以展示该方法的有效性。
Phase retrieval is an important problem with significant physical and industrial applications. In this paper, we consider the case where the magnitude of the measurement of an underlying signal is corrupted by Gaussian noise. We introduce a convex augmentation approach for phase retrieval based on total variation regularization. In contrast to popular convex relaxation models like PhaseLift, our model can be efficiently solved by a modified semi-proximal alternating direction method of multipliers (sPADMM). The modified sPADMM is more general and flexible than the standard one, and its convergence is also established in this paper. Extensive numerical experiments are conducted to showcase the effectiveness of the proposed method.
