scholarly journals Enhanced Phase Retrieval Method Based on Random Phase Modulation

2020 ◽  
Vol 10 (3) ◽  
pp. 1184 ◽  
Author(s):  
Fanxing Li ◽  
Wei Yan ◽  
Fupin Peng ◽  
Simo Wang ◽  
Jialin Du
Keyword(s):  
Phase Modulation ◽  
Phase Retrieval ◽  
Optimal Allocation ◽  
Random Phase ◽  
Modulation Method ◽  
Reconstruction Algorithms ◽  
Rapid Convergence ◽  
Retrieval Method ◽  
Reconstruction Performance ◽  
Serial Algorithm

The phase retrieval method based on random phase modulation can wipe out any ambiguity and stagnation problem in reconstruction. However, the two existing reconstruction algorithms for the random phase modulation method are suffering from problems. The serial algorithm from the spread-spectrum phase retrieval method can realize rapid convergence but has poor noise immunity. Although there is a parallel framework that can suppress noise, the convergence speed is slow. Here, we propose a random phase modulation phase retrieval method based on a serial–parallel cascaded reconstruction framework to simultaneously achieve quality imaging and rapid convergence. The proposed serial–parallel cascaded method uses the phased result from the serial algorithm to serve as the initialization of the subsequent parallel process. Simulations and experiments demonstrate that the superiorities of both serial and parallel algorithms are fetched by the proposed serial–parallel cascaded method. In the end, we analyze the effect of iteration numbers from the serial process on the reconstruction performance to find the optimal allocation scope of iteration numbers.

scholarly journals High-Resolution ISAR Imaging and Autofocusing via 2D-ADMM-Net

2021 ◽  
Vol 13 (12) ◽  
pp. 2326
Author(s):  
Xiaoyong Li ◽  
Xueru Bai ◽  
Feng Zhou
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Phase Error ◽  
Random Phase ◽  
Back Propagation ◽  
Estimation Method ◽  
Data Driven ◽  
Reconstruction Performance ◽  
Imaging Results ◽  
Isar Imaging

A deep-learning architecture, dubbed as the 2D-ADMM-Net (2D-ADN), is proposed in this article. It provides effective high-resolution 2D inverse synthetic aperture radar (ISAR) imaging under scenarios of low SNRs and incomplete data, by combining model-based sparse reconstruction and data-driven deep learning. Firstly, mapping from ISAR images to their corresponding echoes in the wavenumber domain is derived. Then, a 2D alternating direction method of multipliers (ADMM) is unrolled and generalized to a deep network, where all adjustable parameters in the reconstruction layers, nonlinear transform layers, and multiplier update layers are learned by an end-to-end training through back-propagation. Since the optimal parameters of each layer are learned separately, 2D-ADN exhibits more representation flexibility and preferable reconstruction performance than model-driven methods. Simultaneously, it is able to better facilitate ISAR imaging with limited training samples than data-driven methods owing to its simple structure and small number of adjustable parameters. Additionally, benefiting from the good performance of 2D-ADN, a random phase error estimation method is proposed, through which well-focused imaging can be acquired. It is demonstrated by experiments that although trained by only a few simulated images, the 2D-ADN shows good adaptability to measured data and favorable imaging results with a clear background can be obtained in a short time.

scholarly journals Measurement of Small-Slope Free-Form Optical Surfaces with the Modified Phase Retrieval

Micromachines ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 82
Author(s):  
Xinxue Ma ◽  
Jianli Wang ◽  
Bin Wang ◽  
Xinyue Liu
Keyword(s):  
Wave Front ◽  
Phase Retrieval ◽  
Computation Time ◽  
Experimental Arrangement ◽  
Free Form ◽  
Retrieval Algorithm ◽  
Retrieval Method ◽  
Optical Surfaces ◽  
Image Capturing ◽  
Phase Retrieval Algorithm

In this paper, we demonstrate the use of the modified phase retrieval as a method for application in the measurement of small-slope free-form optical surfaces. This technique is a solution for the measurement of small-slope free-form optical surfaces, based on the modified phase retrieval algorithm, whose essence is that only two defocused images are needed to estimate the wave front with an accuracy similar to that of the traditional phase retrieval but with less image capturing and computation time. An experimental arrangement used to measure the small-slope free-form optical surfaces using the modified phase retrieval is described. The results of these experiments demonstrate that the modified phase retrieval method can achieve measurements comparable to those of the standard interferometer.

scholarly journals Two-Step Phase Retrieval Algorithm Using Single-Intensity Measurement

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Cheng Zhang ◽  
Meiqin Wang ◽  
Qianwen Chen ◽  
Dong Wang ◽  
Sui Wei
Keyword(s):  
Phase Retrieval ◽  
Fourier Spectrum ◽  
Retrieval Algorithm ◽  
Improved Method ◽  
Retrieval Method ◽  
Intensity Measurements ◽  
Successful Recovery ◽  
Reconstruction Quality ◽  
Low Probability ◽  
Phase Retrieval Algorithm

Aiming at the problem that the single-intensity phase retrieval method has poor reconstruction quality and low probability of successful recovery, an improved method is proposed in this paper. Our method divides the phase retrieval into two steps: firstly, the GS algorithm is used to recover the amplitude in the spatial domain from the single-spread Fourier spectrum, and then the classical GS algorithm using two intensity measurements (one is recorded and the other is estimated from the first step) measurements is used to recover the phase. Finally, the effectiveness of the proposed method is verified by numerical experiments.

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