Virtual optics and sensing of the retrieved complex field in the back focal plane using a constrained defocus algorithm

Shen, Mengqi; Chow, Terry W.K.; Shen, Hong; Somekh, Michael Geoffrey

doi:10.1364/oe.404573

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…only the other was amplitude. For all experiments, the camera is placed about 3 cm behind the focal plane in order to reduce the dynamic range of the Fourier intensity images [19]. The central 200 × 200 pixels of the DMD are used for the implementation of the noise masks.…”

Section: Resultsmentioning

confidence: 99%

Robust Phase Retrieval with Green Noise Binary Masks

Ye,

Chan,

Somekh

et al. 2020

Preprint

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Phase retrieval with pre-defined optical masks can provide extra constraint and thus achieve improved performance. The recent progress in optimization theory demonstrates the superiority of random masks in phase retrieval algorithms. However, traditional approaches just focus on the randomness of the masks but ignore their non-bandlimited nature. When using these masks in the reconstruction process for phase retrieval, the high frequency part of the masks is often removed in the process and thus leads to degraded performance. Based on the concept of digital halftoning, this paper proposes a green noise binary masking scheme which can greatly reduce the high frequency content of the masks while fulfilling the randomness requirement. The experimental results show that the proposed green noise binary masking scheme outperform the traditional ones when using in a DMD-based coded diffraction pattern phase retrieval system.

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Section: Resultsmentioning

confidence: 99%

Robust Phase Retrieval with Green Noise Binary Masks

Ye,

Chan,

Somekh

et al. 2020

Preprint

Self Cite

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Non-iterative aberration retrieval based on the spot shape around focus

Smid

See

Somekh

et al. 2022

Optics and Lasers in Engineering

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Deep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive index sensing application

Thadson

Visitsattapongse

Pechprasarn

2021

Sci Rep

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A deep learning algorithm for single-shot phase retrieval under a conventional microscope is proposed and investigated. The algorithm has been developed using the context aggregation network architecture; it requires a single input grayscale image to predict an output phase profile through deep learning-based pattern recognition. Surface plasmon resonance imaging has been employed as an example to demonstrate the capability of the deep learning-based method. The phase profiles of the surface plasmon resonance phenomena have been very well established and cover ranges of phase transitions from 0 to 2π rad. We demonstrate that deep learning can be developed and trained using simulated data. Experimental validation and a theoretical framework to characterize and quantify the performance of the deep learning-based phase retrieval method are reported. The proposed deep learning-based phase retrieval performance was verified through the shot noise model and Monte Carlo simulations. Refractive index sensing performance comparing the proposed deep learning algorithm and conventional surface plasmon resonance measurements are also discussed. Although the proposed phase retrieval-based algorithm cannot achieve a typical detection limit of 10–7 to 10–8 RIU for phase measurement in surface plasmon interferometer, the proposed artificial-intelligence-based approach can provide at least three times lower detection limit of 4.67 × 10–6 RIU compared to conventional intensity measurement methods of 1.73 × 10–5 RIU for the optical energy of 2500 pJ with no need for sophisticated optical interferometer instrumentation.

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Virtual optics and sensing of the retrieved complex field in the back focal plane using a constrained defocus algorithm

Cited by 5 publications

References 19 publications

Robust Phase Retrieval with Green Noise Binary Masks

Robust Phase Retrieval with Green Noise Binary Masks

Non-iterative aberration retrieval based on the spot shape around focus

Deep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive index sensing application

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