论文标题
班级特异性的盲目反向跨期检索
Class-Specific Blind Deconvolutional Phase Retrieval Under a Generative Prior
论文作者
论文摘要
在本文中，我们考虑了从其圆形卷积的无相位测量中共同恢复两个实用值的信号的高度错误的问题。问题出现在各种成像方式中，例如傅立叶Ptychography，X射线晶体学和可见光通信。我们建议使用在两个预处理的深层生成网络下，使用交替的梯度下降算法来解决这个反问题。一个接受了尖锐图像的训练，另一个是在模糊内核上进行的。所提出的恢复算法努力在相应的前生成剂范围内找到尖锐的图像和模糊内核，这些遗传范围\ textit {best}解释了正向测量模型。在此过程中，我们能够重建质量图像估计值。此外，数字表明，提出的方法在反映物理上可实现的成像系统的具有挑战性的测量模型上表现良好，并且对噪声也很强
In this paper, we consider the highly ill-posed problem of jointly recovering two real-valued signals from the phaseless measurements of their circular convolution. The problem arises in various imaging modalities such as Fourier ptychography, X-ray crystallography, and in visible light communication. We propose to solve this inverse problem using alternating gradient descent algorithm under two pretrained deep generative networks as priors; one is trained on sharp images and the other on blur kernels. The proposed recovery algorithm strives to find a sharp image and a blur kernel in the range of the respective pre-generators that \textit{best} explain the forward measurement model. In doing so, we are able to reconstruct quality image estimates. Moreover, the numerics show that the proposed approach performs well on the challenging measurement models that reflect the physically realizable imaging systems and is also robust to noise
