The Phase Retrieval Problem

1981 ◽  
Vol 28 (6) ◽  
pp. 735-738 ◽  
Author(s):  
J.G. Walker
Keyword(s):  
Phase Retrieval ◽  
Retrieval Problem

scholarly journals Complex wavefront sensing based on coherent diffraction imaging using vortex modulation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rujia Li ◽  
Liangcai Cao
Keyword(s):  
Phase Retrieval ◽  
Dynamic Range ◽  
A Priori ◽  
Measured Intensity ◽  
Physical Constraints ◽  
Coherent Diffraction Imaging ◽  
Effective Dynamic ◽  
Ill Posed ◽  
Retrieval Problem ◽  
Diffraction Imaging

AbstractPhase retrieval seeks to reconstruct the phase from the measured intensity, which is an ill-posed problem. A phase retrieval problem can be solved with physical constraints by modulating the investigated complex wavefront. Orbital angular momentum has been recently employed as a type of reliable modulation. The topological charge l is robust during propagation when there is atmospheric turbulence. In this work, topological modulation is used to solve the phase retrieval problem. Topological modulation offers an effective dynamic range of intensity constraints for reconstruction. The maximum intensity value of the spectrum is reduced by a factor of 173 under topological modulation when l is 50. The phase is iteratively reconstructed without a priori knowledge. The stagnation problem during the iteration can be avoided using multiple topological modulations.

Phase Retrieval Problem in Fractional Fourier Domain

2010 ◽  
Vol 47 (8) ◽  
pp. 081001
Author(s):  
廖天河 Liao Tianhe ◽  
高穹 Gao Qiong ◽  
崔远峰 Cui Yuanfeng ◽  
宋凯洋 Song Kaiyang
Keyword(s):  
Phase Retrieval ◽  
Fourier Domain ◽  
Retrieval Problem

On the ambiguities of phase retrieval problem in Fourier ptychographic microscopy

2020 ◽  
Author(s):  
Leng Ningyi ◽  
Yuan Ziyang ◽  
Yang Haoxing ◽  
Hongxia Wang ◽  
Du Longkun
Keyword(s):  
Phase Retrieval ◽  
Retrieval Problem

scholarly journals The nonsmooth landscape of phase retrieval

2020 ◽  
Vol 40 (4) ◽  
pp. 2652-2695
Author(s):  
Damek Davis ◽  
Dmitriy Drusvyatskiy ◽  
Courtney Paquette
Keyword(s):  
Phase Retrieval ◽  
Optimal Solution ◽  
Subgradient Method ◽  
Relative Distance ◽  
Stationary Points ◽  
Image Recovery ◽  
The Real ◽  
Codimension Two ◽  
Statistical Assumptions ◽  
Retrieval Problem

Abstract We consider a popular nonsmooth formulation of the real phase retrieval problem. We show that under standard statistical assumptions a simple subgradient method converges linearly when initialized within a constant relative distance of an optimal solution. Seeking to understand the distribution of the stationary points of the problem, we complete the paper by proving that as the number of Gaussian measurements increases, the stationary points converge to a codimension two set, at a controlled rate. Experiments on image recovery problems illustrate the developed algorithm and theory.

The phase retrieval problem

1995 ◽  
Vol 11 (1) ◽  
pp. 1-28 ◽  
Author(s):  
M V Klibanov ◽  
P E Sacks ◽  
A V Tikhonravov
Keyword(s):  
Phase Retrieval ◽  
Retrieval Problem
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