Spiral phase contrast imaging in nonlinear optics: seeing phase objects using invisible illumination

Qiu, Xiaodong; Li, Fangshu; Zhang, Wuhong; Zhu, Zhi‐Han; Chen, Lixiang

doi:10.1364/optica.5.000208

Cited by 183 publications

(98 citation statements)

References 25 publications

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“…Based on the QPM, we can obtain the up-conversion imaging in any arbitrary wavelength by designing the QPM period of crystal and coating differently. Recent work of SPC up-conversion imaging with second harmonic generation using Potassium Titanyl Phosphate (KTP) crystal based on critically type Ⅱ phase-matching was achieved by Chen's group [18]. It is the first time that the SPC imaging technique has been realized in nonlinear optical process.…”

Section: ( )mentioning

confidence: 99%

Up-Conversion Imaging Processing With Field-of-View and Edge Enhancement

Liu

Zhou

et al. 2019

Phys. Rev. Applied

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Spiral phase contrast is an important and convenient imaging processing technology in edge detection, and a broader field-of-view (FOV) of imaging is a long-pursuing aim to see more regions of the illumination objects. Compared with near-infrared (NIR) spectrum, the up-conversion imaging in visible spectrum benefits from the advantages of higher efficiency detection and lower potential speckle. FOV enhanced and spiral phase contrast up-conversion imaging processing methods by using second order nonlinear frequency up-conversion from NIR spectrum to visible spectrum in two different configurations are presented in this work. By changing the temperature of crystal, controllable spatial patterns of imaging with more than 4.5 times enhancement of FOV is realized in both configurations. Additionally, we present numerical simulations of the phenomenon, which agree well with the experimental observations. Our results provide a very promising way in imaging processing, which may be widely used in biomedicine, remote sensing and up-conversion monitoring.

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Section: ( )mentioning

confidence: 99%

Up-Conversion Imaging Processing With Field-of-View and Edge Enhancement

Liu

Zhou

et al. 2019

Phys. Rev. Applied

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“…Following precise alignment with the LSFM system, a fully automated image acquisition was implemented via a customized LabVIEW program. We created a virtual spiral phase plate (SPP) for automatic edge detection by performing the forward and inverse Fourier transform in MATLAB (MathWorks, Inc.) [48,49]. The hardware system is also adaptable for inserting a real SPP along with optical components (Ob2 and TL2) as a high-pass filter for edge detection.…”

Section: Light-sheet Microscope Designmentioning

confidence: 99%

Saak Transform-Based Machine Learning for Light-Sheet Imaging of Cardiac Trabeculation

Ding

Gudapati

Lin

et al. 2019

Preprint

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AbstractRecent advances in light-sheet fluorescence microscopy (LSFM) enable 3-dimensional (3-D) imaging of cardiac architecture and mechanics in toto. However, segmentation of the cardiac trabecular network to quantify cardiac injury remains a challenge. We hereby employed “subspace approximation with augmented kernels (Saak) transform” for accurate and efficient quantification of the light-sheet image stacks following chemotherapy-treatment. We established a machine learning framework with augmented kernels based on the Karhunen-Loeve Transform (KLT) to preserve linearity and reversibility of rectification. The Saak transform-based machine learning enhances computational efficiency and obviates iterative optimization of cost function needed for neural networks, minimizing the number of training data sets to three 2-D slices for segmentation in our scenario. The integration of forward and inverse Saak transforms serves as a light-weight module to filter adversarial perturbations and reconstruct estimated images, salvaging robustness of existing classification methods. The accuracy and robustness of the Saak transform are evident following the tests of dice similarity coefficients and various adversary perturbation algorithms, respectively. The addition of edge detection further allows for quantifying the surface area to volume ratio (SVR) of the myocardium in response to chemotherapy-induced cardiac remodeling. The combination of Saak transform, random forest, and edge detection augments segmentation efficiency by 20-fold as compared to manual processing; thus, establishing a robust framework for post light-sheet imaging processing, creating a data-driven machine learning for 3-D quantification of cardiac ultra-structure.

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“…Zernike invented the phase contrast microscopy [1] to render these transparent objects but without the ability to quantify the phase distribution. Later, in order to enhance the image contrast, Nomarski prisms were created for differential interference contrast (DIC) imaging [2] which offers the phase gradient information [3][4][5][6][7]. Further, various quantitative phase measurement technologies were developed for mapping the optical thickness of specimens [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, by tuning a uniform constant background as the bias, we create a virtual light source to render the measured images with a shadow-cast effect and further quantify the phase distribution of a coherent field. Without complex modulation devices [3][4][5][6][7][8][9][10], our method offers great simplicity and flexibility. Importantly, since the proposed method is irrelevant to resonance or material dispersion, it works in general wavelength with large temporal bandwidths which is very suitable for highthroughput real-time image processing.…”

Section: Introductionmentioning

confidence: 99%

Optical phase mining by adjustable spatial differentiator

2020

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Phase is a fundamental resource for optical imaging but cannot be directly observed with intensity measurements. The existing methods to quantify a phase distribution rely on complex devices and structures. Here we experimentally demonstrate a phase mining method based on so-called adjustable spatial differentiation, just generally by analyzing the polarization in light reflection on a single planar dielectric interface. With introducing an adjustable bias, we create a virtual light source to render the measured images with a shadow-cast effect. We further successfully recover the phase distribution of a transparent object from the virtual shadowed images. Without any dependence on resonance or material dispersion, this method directly stems from the intrinsic properties of light and can be generally extended to a board frequency range. arXiv:1911.00861v1 [physics.optics]

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Spiral phase contrast imaging in nonlinear optics: seeing phase objects using invisible illumination

Cited by 183 publications

References 25 publications

Up-Conversion Imaging Processing With Field-of-View and Edge Enhancement

Up-Conversion Imaging Processing With Field-of-View and Edge Enhancement

Saak Transform-Based Machine Learning for Light-Sheet Imaging of Cardiac Trabeculation

Optical phase mining by adjustable spatial differentiator

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