Phase retrieval utilizing geometric average and stochastic perturbation

Sun, Mingjie; Zhang, Jia-Min

doi:10.1016/j.optlaseng.2019.02.007

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…In computational imaging, phase retrieval (PR) is a tool to reconstruct a wavefront with diffraction images [1][2][3][4][5][6]. Recently, multi-intensity iterative algorithms [7][8][9][10] have shown stronger noise robustness, however, it is easy to bring an aliasing artifact into the system.…”

Section: Introductionmentioning

confidence: 99%

Image Reconstruction Using Autofocus in Single-Lens System

Zhou

Xiu

et al. 2022

Applied Sciences

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To reconstruct the wavefront in a single-lens coherent diffraction imaging (CDI) system, we propose a closed-loop cascaded iterative engine (CIE) algorithm based on the known information of the imaging planes. The precision of diffraction distance is an important prerequisite for a perfect reconstruction of samples. For coherent diffraction imaging with a lens, autofocus is investigated to accurately determine the object distance and image distance. For the case of only the object distance being unknown, a diffuser is used to scatter the coherent beam for speckle illumination to improve the performance of autofocus. The optimal object distance is obtained stably and robustly by combing speckle imaging with clarity evaluation functions. SSIM and MSE, using the average pixel value of the reconstructed data set as a reference, are applied on two-unknown-distance autofocus. Simulation and experiment results are presented to prove the feasibility of the CIE and proposed auto-focusing method.

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Section: Introductionmentioning

confidence: 99%

Image Reconstruction Using Autofocus in Single-Lens System

Zhou

Xiu

et al. 2022

Applied Sciences

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“…However, the success of GHIO depends strongly on multiple runs just like HIO, which come at the cost of more computation. Another multiple initials algorithm utilized particle swarm optimization (PSO) in HIO, which makes random initial particles learn and renew each other, and therefore increases the possibility of reaching the global minimum [11,12]. However, the random nature of stochastic perturbations jeopardized the efficiency of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Simulated Annealing Applied to HIO Method for Phase Retrieval

Zhang

Sun

2021

Photonics

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Phase retrieval utilizing Fourier amplitudes plays a significant role in image recovery. Iterative phase retrieval algorithms have been developed to retrieve phase information that cannot be recorded by detectors directly. However, iterative algorithms face the problem of being trapped in local minima due to the nonconvexity of phase retrieval, and most existing works addressed this by optimizing in multiple runs parallelly to improve the possibility that one of these could reach the global minimum. Alternatively, we propose in this work to increase the probability of reaching the global minimum with one arbitrary initial distribution by adapting simulated annealing in the standard hybrid input-output (HIO) algorithm. Numerical and experimental results demonstrate that the proposed method reconstructs images with mean square errors 50.12% smaller than those reconstructed by HIO. More importantly, the proposed method can be applied to any HIO-based algorithm with multiple runs to further improve the performance.

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“…Therefore these methods can get a high-precision and stable solution without initial guess and support area. Multi-image iterative phase retrieval methods [17][18][19][20] have been applied in wavefront reconstruction [21], x-ray imaging [22], diffraction tomography [23], biological imaging [24,25], and encryption [26,27].…”

Section: Introductionmentioning

confidence: 99%

A noise-robust multi-intensity phase retrieval method based on structural patch decomposition

Zhang¹,

Guo

Zhai

et al. 2020

J. Opt.

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Multi-image iterative parallel phase retrieval methods are widely used in measurement and imaging, due to their simplicity and effectiveness. However, the retrieved image will have foggy diffuse noise, since some unknown cross-talk factors exist in iterative calculation. In this paper, a new updated method is embedded into the calculation process of phase retrieval. The iterative strategy of phase retrieval is updated to enhance robustness for noisy images. Experimental and simulated results have demonstrated that the improved algorithm has faster convergence speed and higher anti-noise performance.

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Phase retrieval utilizing geometric average and stochastic perturbation

Cited by 6 publications

References 17 publications

Image Reconstruction Using Autofocus in Single-Lens System

Image Reconstruction Using Autofocus in Single-Lens System

Simulated Annealing Applied to HIO Method for Phase Retrieval

A noise-robust multi-intensity phase retrieval method based on structural patch decomposition

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