Lensfree on-chip microscopy based on single-plane phase retrieval

Guo, Cheng; Liu, Xianming; Zhang, Feilong; Du, Yongbin; Zheng, Shouzhu; Wang, Zehua; Zhang, Xiaoqing; Kan, Xinchi; Liu, Zhengjun; Wang, Weibo

doi:10.1364/oe.458400

Cited by 15 publications

(7 citation statements)

References 62 publications

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“…An algorithm by Guo et al 18 adds regularization to the optimization problem in the form of both a Total Variation (TV) norm in combination with regularization by denoising. 19 The TV norm regularizer functions as an effective edge-preserving noise remover, smoothing signals in flat regions while maintaining important highfrequency information.…”

Section: Iterative Reconstruction Methodsmentioning

confidence: 99%

Review of the state-of-the-art of in-line holographic microscopy

Madsen,

Panah,

Larsen

et al. 2023

Complex Light and Optical Forces XVII

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Section: Iterative Reconstruction Methodsmentioning

confidence: 99%

Review of the state-of-the-art of in-line holographic microscopy

Madsen,

Panah,

Larsen

et al. 2023

Complex Light and Optical Forces XVII

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“…In lens-less holographic microscopy, the conjugate image is unavoidable. The simplest single-frame phase recovery method is an iterative phase recovery method based on support domain constraints [ 49 , 50 , 51 , 52 , 53 ]. The key to this approach is the support field/mask of the object plane.…”

Section: Lens-less Microscopymentioning

confidence: 99%

Computational Portable Microscopes for Point-of-Care-Test and Tele-Diagnosis

Bian

Xing

Jiao

et al. 2022

Cells

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In bio-medical mobile workstations, e.g., the prevention of epidemic viruses/bacteria, outdoor field medical treatment and bio-chemical pollution monitoring, the conventional bench-top microscopic imaging equipment is limited. The comprehensive multi-mode (bright/dark field imaging, fluorescence excitation imaging, polarized light imaging, and differential interference microscopy imaging, etc.) biomedical microscopy imaging systems are generally large in size and expensive. They also require professional operation, which means high labor-cost, money-cost and time-cost. These characteristics prevent them from being applied in bio-medical mobile workstations. The bio-medical mobile workstations need microscopy systems which are inexpensive and able to handle fast, timely and large-scale deployment. The development of lightweight, low-cost and portable microscopic imaging devices can meet these demands. Presently, for the increasing needs of point-of-care-test and tele-diagnosis, high-performance computational portable microscopes are widely developed. Bluetooth modules, WLAN modules and 3G/4G/5G modules generally feature very small sizes and low prices. And industrial imaging lens, microscopy objective lens, and CMOS/CCD photoelectric image sensors are also available in small sizes and at low prices. Here we review and discuss these typical computational, portable and low-cost microscopes by refined specifications and schematics, from the aspect of optics, electronic, algorithms principle and typical bio-medical applications.

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“…Sparsity priors in various domains such as the spatial [44][45][46][47][48][49], gradient [50][51][52][53][54][55][56][57][58][59][60][61], wavelet [62] and other domains [63,64] or with a dictionary-learned transform [65][66][67] have been demonstrated as effective regularizers for phase recovery. More recently, implicit image priors from advanced denoisers such as BM3D [68][69][70][71][72][73] or represented by deep neural networks [74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92]…”

Section: Introductionmentioning

confidence: 99%

Iterative projection meets sparsity regularization: towards practical single-shot quantitative phase imaging with in-line holography

Gao¹,

Cao²

2023

gxjzz

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Holography provides access to the optical phase. The emerging compressive phase retrieval approach can achieve in-line holographic imaging beyond the information-theoretic limit or even from a single shot by exploring the signal priors. However, iterative projection methods based on physical knowledge of the wavefield suffer from poor imaging quality, whereas the regularization techniques sacrifice robustness for fidelity. In this work, we present a unified compressive phase retrieval framework for in-line holography that encapsulates the unique advantages of both physical constraints and sparsity priors. In particular, a constrained complex total variation (CCTV) regularizer is introduced that explores the well-known absorption and support constraints together with sparsity in the gradient domain, enabling practical high-quality in-line holographic imaging from a single intensity image. We developed efficient solvers based on the proximal gradient method for the non-smooth regularized inverse problem and the corresponding denoising subproblem. Theoretical analyses further guarantee the convergence of the algorithms with prespecified parameters, obviating the need for manual parameter tuning. As both simulated and optical experiments demonstrate, the proposed CCTV model can characterize complex natural scenes while utilizing physically tractable constraints for quality enhancement. This new compressive phase retrieval approach can be extended, with minor adjustments, to various imaging configurations, sparsifying operators, and physical knowledge. It may cast new light on both theoretical and empirical studies.

show abstract

Lensfree on-chip microscopy based on single-plane phase retrieval

Cited by 15 publications

References 62 publications

Review of the state-of-the-art of in-line holographic microscopy

Review of the state-of-the-art of in-line holographic microscopy

Computational Portable Microscopes for Point-of-Care-Test and Tele-Diagnosis

Iterative projection meets sparsity regularization: towards practical single-shot quantitative phase imaging with in-line holography

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