Single-shot differential phase contrast quantitative phase imaging method based on color multiplexed illumination

    公开(公告)号:US11893719B2

    公开(公告)日:2024-02-06

    申请号:US17766088

    申请日:2020-08-18

    CPC classification number: G06T5/50 G02B21/367

    Abstract: A single-shot differential phase contrast quantitative phase imaging method based on color multiplexing illumination. A color multiplexing illumination solution is used to realize single-shot differential phase contrast quantitative phase imaging. In the single-shot color multiplexing illumination solution, three illumination wavelengths of red, green, and blue are used to simultaneously illuminate a sample, and the information of the sample in multiple directions is converted into intensity information on different channels of a color image. By performing channel separation on this color image, the information about the sample at different spatial frequencies can be obtained. Such a color multiplexing illumination solution requires only one acquired image, thus enhancing the transfer response of the phase transfer function of single-shot differential phase contrast imaging in the entire frequency range, and achieving real-time dynamic quantitative phase imaging with a high contrast, a high resolution, and a high stability. In addition, an alternate illumination strategy is provided, so that a completely isotropic imaging resolution at the limit acquisition speed of the camera can be achieved.

    Highly efficient three-dimensional image acquisition method based on multi-mode composite encoding and epipolar constraint

    公开(公告)号:US10911672B2

    公开(公告)日:2021-02-02

    申请号:US16496845

    申请日:2018-02-26

    Abstract: A highly efficient three-dimensional image acquisition method based on multi-mode composite encoding and epipolar constraint, respectively using a fast imaging mode or a high-precision imaging mode, wherein in the fast imaging mode, two phase maps having different frequencies are obtained by four stripe gratings, and a high-frequency absolute phase is obtained by means of the epipolar constraint and a left-right consistency check, and the three-dimensional image is obtained by means of a mapping relationship between the phase and three-dimensional coordinates; and in the high precision imaging mode, two phases having different frequencies are obtained by means of N+2 stripe gratings, a low-frequency absolute phase is obtained by the epipolar constraint, and the unwrapping of a high-frequency phase is assisted by means of the low-frequency absolute phase, so as to obtain the high-frequency absolute phase, and finally, the three-dimensional image is obtained by the mapping relationship between the phase and the three-dimensional coordinates. In this way, the imaging efficiency is ensured, and the imaging precision is improved.

    A THREE-DIMENSIONAL MEASUREMENT METHOD BASED ON END-TO-END DEEP LEARNING FOR SPECKLE PROJECTION

    公开(公告)号:US20240020866A1

    公开(公告)日:2024-01-18

    申请号:US18025815

    申请日:2021-08-18

    Abstract: The invention discloses a three-dimensional (3D) measurement method based on end-to-end deep learning for speckle projection. First, the speckle pattern was projected by the projector and collected simultaneously by the stereo camera. The speckle images after stereo rectification are fed into the stereo matching network. A feature extraction sub-network based on shared weights processes the speckle images to obtain a series of low-resolution 3D feature tensors, The feature tensor is fed into the saliency object detection sub-network to detect foreground information in the speckle images, producing a full-resolution valid mask map. A 4D matching cost volume is generated using the feature tensor of both views based on the candidate disparity range, filtered by a series of 3D convolutional layers to achieve cost aggregation, so that the initial disparity map is obtained by disparity regression. The final disparity map is obtained by combining the mask map and the initial disparity map to achieve a single-frame, robust, and absolute 3D shape measurement. The invention achieves a single-frame, robust, and absolute 3D shape measurement by projecting a single speckle pattern.

    Programmable annular led illumination-based high efficiency quantitative phase microscopy imaging method

    公开(公告)号:US11555992B2

    公开(公告)日:2023-01-17

    申请号:US16633037

    申请日:2018-02-26

    Abstract: The invention discloses a programmable annular LED illumination-based high efficiency quantitative phase microscopy imaging method, the proposed method comprising the following steps: the derivation of system optical transfer function in a partially coherent illumination imaging system; the derivation of phase transfer function with the weak object approximation under the illumination of tilted axially symmetric coherent point illumination source; the extension of illumination from an axially symmetric coherence point source to a discrete annular point source, and the optical transfer function can be treated as an incoherent superposition of each pair of tilted axially symmetric coherent point sources. The acquisition of raw intensity dataset; the implementation of deconvolution for quantitative phase reconstruction. The invention derives the system phase transfer function under the tilted axially symmetric point light source in the case of partially coherent illumination, and promotes the optical phase transfer function of the discrete annular point light source. The programmability characteristic of LED array enables the annular illumination aperture to be flexibly adjustable, being applicable to different microscopic objects with different numerical apertures, and improving the compatibility and flexibility of the system.

    Super-rapid three-dimensional topography measurement method and system based on improved fourier transform contour technique

    公开(公告)号:US11029144B2

    公开(公告)日:2021-06-08

    申请号:US16496815

    申请日:2018-02-26

    Abstract: A super-rapid three-dimensional measurement method and system based on an improved Fourier transform contour technique is disclosed. The method comprises: firstly calibrating a measurement system to obtain calibration parameters, then cyclically projecting 2n patterns into a measured scene using a projector, wherein n patterns are binary sinusoidal fringes with different high frequency, and the other n patterns are all-white images with the values of 1, and projecting the all-white images between every two binary high-frequency sinusoidal fringes, and synchronously acquiring images using a camera; and then performing phase unwrapping on wrapped phases to obtain initial absolute phases, and correcting the initial absolute phases, and finally reconstructing a three-dimensional topography of the measured scene by exploiting the corrected absolute phases and the calibration parameters to obtain 3D spatial coordinates of the measured scene in a world coordinate system, thereby accomplishing three-dimensional topography measurement of an object. In this way, the precision of three-dimensional topography measurement is ensured, and the speed of three-dimensional topography measurement is improved.

    Deep learning-based temporal phase unwrapping method for fringe projection profilometry

    公开(公告)号:US11906286B2

    公开(公告)日:2024-02-20

    申请号:US17280464

    申请日:2019-07-05

    CPC classification number: G01B11/25 G06N3/049 G06N3/08

    Abstract: The invention discloses a deep learning-based temporal phase unwrapping method for fringe projection profilometry. First, four sets of three-step phase-shifting fringe patterns with different frequencies (including 1, 8, 32, and 64) are projected to the tested objects. The three-step phase-shifting fringe images acquired by the camera are processed to obtain the wrapped phase map using a three-step phase-shifting algorithm. Then, a multi-frequency temporal phase unwrapping (MF-TPU) algorithm is used to unwrap the wrapped phase map to obtain a fringe order map of the high-frequency phase with 64 periods. A residual convolutional neural network is built, and its input data are set to be the wrapped phase maps with frequencies of 1 and 64, and the output data are set to be the fringe order map of the high-frequency phase with 64 periods. Finally, the training dataset and the validation dataset are built to train and validate the network. The network makes predictions on the test dataset to output the fringe order map of the high-frequency phase with 64 periods. The invention exploits a deep learning method to unwrap a wrapped phase map with a frequency of 64 using a wrapped phase map with a frequency of 1 and obtain an absolute phase map with fewer phase errors and higher accuracy.

    A MINIATURIZED, LOW-COST, MULTI-CONTRAST LABEL-FREE MICROSCOPE IMAGING SYSTEM

    公开(公告)号:US20230359010A1

    公开(公告)日:2023-11-09

    申请号:US18026276

    申请日:2021-08-18

    CPC classification number: G02B21/14 G02B21/0008 G02B21/0032 G02B21/008

    Abstract: The invention discloses a miniaturized, low-cost, multi-contrast label-free microscopic imaging system. The imaging system is based on an inverted microscopic structure, a highly integrated optical system is designed by adopting a micro lens having a fixed focal length, and a complex optical system of a traditional microscope system is replaced, such that the whole microscope is highly integrated. The system uses a programmable LED array as an illumination light source the LED array is controlled by a computer to display different illumination modes, six imaging functions of a bright field, a dark field a rainbow dark field, Rheinberg optical dyeing, differential phase contrast, and quantitative phase imaging are achieved; and diversified unmarked imaging methods are provided for biological applications. The invention provides a matching control system, which can realize system hardware control and algorithm execution and display, comprises functions such as illumination control, camera parameter adjustment quantitative phase reconstruction recovery, two-dimensional/three-dimensional result display, and quantitative profile analysis, and can realize diversified information obtaining and analysis of unmarked samples.

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