Generalized optimization framework for pixel super-resolution imaging in digital holography

Gao, Yunhui; Cao, Liangcai

doi:10.1364/oe.434449

Cited by 29 publications

(28 citation statements)

References 34 publications

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“…It has been observed by many prior works that minimizing the lower-order amplitude-based fidelity function leads to faster convergence compared with minimizing the intensity-based one [59]. In analogy to the classical phase retrieval, the lower-order fidelity function in Equation ( 2) was adopted in [33] for PSR phase retrieval, whose superior performance was also experimentally verified.…”

Section: Regularized Inversionmentioning

confidence: 99%

“…The proximal gradient method is adopted for solving the non-smooth composite optimization problem of Equation ( 2), which proceeds by minimizing the two terms in an alternative manner [66]. Specifically, we apply a gradient update step with respect to the fidelity term, whose Wirtinger gradient is given by [33]…”

Section: Accelerated Wirtinger Flowmentioning

confidence: 99%

“…The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/cells11131999/s1. References [33,75,80,81] are cited in the Supplementary Materials.…”

Section: Supplementary Materialsmentioning

confidence: 99%

“…In recent years, it has been recognized from a physical perspective that the missing phase and the undersampled high-frequency information can be both encoded into the intensity observations via diversity measurements [31][32][33], which can be implemented by varying the defocus distances [34][35][36], illumination wavelengths [37][38][39][40], modulation patterns [41][42][43], and probe positions [44][45][46], etc., making it possible for numerical recovery. On the algorithmic side, PSR phase retrieval can be achieved by a simple modification to the classical phase retrieval algorithms [47][48][49].…”

Section: Introductionmentioning

confidence: 99%

“…On the algorithmic side, PSR phase retrieval can be achieved by a simple modification to the classical phase retrieval algorithms [47][48][49]. More recently, PSR phase retrieval has been recast as a standard optimization problem, which allows the use of off-the-shelf optimization tools, such as alternating projection and gradient descent algorithms [33].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Pixel Super-Resolution Phase Retrieval for Lensless On-Chip Microscopy via Accelerated Wirtinger Flow

Gao

Yang

Cao

2022

Cells

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Empowered by pixel super-resolution (PSR) and phase retrieval techniques, lensless on-chip microscopy opens up new possibilities for high-throughput biomedical imaging. However, the current PSR phase retrieval approaches are time consuming in terms of both the measurement and reconstruction procedures. In this work, we present a novel computational framework for PSR phase retrieval to address these concerns. Specifically, a sparsity-promoting regularizer is introduced to enhance the well posedness of the nonconvex problem under limited measurements, and Nesterov’s momentum is used to accelerate the iterations. The resulting algorithm, termed accelerated Wirtinger flow (AWF), achieves at least an order of magnitude faster rate of convergence and allows a twofold reduction in the measurement number while maintaining competitive reconstruction quality. Furthermore, we provide general guidance for step size selection based on theoretical analyses, facilitating simple implementation without the need for complicated parameter tuning. The proposed AWF algorithm is compatible with most of the existing lensless on-chip microscopes and could help achieve label-free rapid whole slide imaging of dynamic biological activities at subpixel resolution.

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Section: Regularized Inversionmentioning

confidence: 99%

Section: Accelerated Wirtinger Flowmentioning

confidence: 99%

“…The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/cells11131999/s1. References [33,75,80,81] are cited in the Supplementary Materials.…”

Section: Supplementary Materialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pixel Super-Resolution Phase Retrieval for Lensless On-Chip Microscopy via Accelerated Wirtinger Flow

Gao

Yang

Cao

2022

Cells

Self Cite

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Compensation of aberrations in holographic microscopes: main strategies and applications

et al. 2022

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Digital holography is a technique that provides a non-invasive, label-free, quantitative, and high-resolution imaging employable in biological and science of matter fields, but not only. In the last decade, digital holography (DH) has undergone very significant signs of progress that made it one of the most powerful metrology tools. However, one of the most important issues to be afforded and solved for obtaining quantitative phase information about the analyzed specimen is related to phase aberrations. Sources of aberrations can be diverse, and several strategies have been developed and tested to make DH a reliable optical system with submicron resolution. This paper reviews the most effective and robust methods to remove or compensate phase aberrations in retrieved quantitative phase imaging by DH. Different strategies are presented and discussed in detail on how to remove or compensate for such disturbing aberrations. Among the various methods improvements in the optical setups are considered the numerical algorithms, the hybrid methods, and the very recent Artificial Intelligence (AI) approaches to compensate for all aberrations which affect the setups to improve the imaging quality and the accuracy of the reconstruction images’ procedures.

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Roadmap on computational methods in optical imaging and holography [invited]

Rosen,

Alford,

Allan

et al. 2024

Appl. Phys. B

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Computational methods have been established as cornerstones in optical imaging and holography in recent years. Every year, the dependence of optical imaging and holography on computational methods is increasing significantly to the extent that optical methods and components are being completely and efficiently replaced with computational methods at low cost. This roadmap reviews the current scenario in four major areas namely incoherent digital holography, quantitative phase imaging, imaging through scattering layers, and super-resolution imaging. In addition to registering the perspectives of the modern-day architects of the above research areas, the roadmap also reports some of the latest studies on the topic. Computational codes and pseudocodes are presented for computational methods in a plug-and-play fashion for readers to not only read and understand but also practice the latest algorithms with their data. We believe that this roadmap will be a valuable tool for analyzing the current trends in computational methods to predict and prepare the future of computational methods in optical imaging and holography.

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Generalized optimization framework for pixel super-resolution imaging in digital holography

Cited by 29 publications

References 34 publications

Pixel Super-Resolution Phase Retrieval for Lensless On-Chip Microscopy via Accelerated Wirtinger Flow

Pixel Super-Resolution Phase Retrieval for Lensless On-Chip Microscopy via Accelerated Wirtinger Flow

Compensation of aberrations in holographic microscopes: main strategies and applications

Roadmap on computational methods in optical imaging and holography [invited]

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