基于压缩传感的纯相位物体相位恢复

传统的相位恢复算法通过双强度或者单强度测量的数据进行迭代运算以恢复丢失的相位信息, 它要求采样数据必须满足香农采样定理.当成像的分辨率较高时, 大量的测量数据势必会对数据采样设备提出更多的要求.因此, 为减少采样负担, 本文提出了一种基于压缩传感的相位恢复算法, 通过在傅里叶面的少量单强度测量数据, 应用改进的混合输入输出算法来恢复纯相位物体的相位分布.在采样数据远小于采样定理所需的条件时, 该算法仍能精确地恢复相位分布具有分块均匀特征的纯相位物体.数值仿真实验表明该算法具有良好的收敛性能. 关键词： 相位恢复 压缩传感

Phase retrieval of pure phase object based on compressed sensing

Traditional phase retrieval algorithm, which iteratively reconstructs the phase from 2-intensity measurement or 1-intensity measurement, requires Shannon sampling theorem to be satisfied. This could lead to more requirements for data storage when high resolution imaging is concerned. In order to lower the sampling budget, in this paper we purpose a compressed sensing based phase retrieval algorithm. Through 1-intensity measurement in Fourier plane, our improved Hybrid I/O algorithm is used to reconstruct the exact phase retribution of pure phase object. The algorighm proposed in this paper can reconstruct piecewise regular phase distributed pure phase object from far less amplitude measurements than ones for which the sampling theorem requires to be satisfied. The simulated data indicate that the algorithm has a good converge performance.
Keywords： phase retrieval compressed sensing