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Готові списки джерел за темами / Fourier Ptychographic Microscopy / Статті в журналах

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Статті в журналах з теми "Fourier Ptychographic Microscopy"

Автор: Grafiati

Опубліковано: 7 вересня 2024

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1

Jizhou Zhang, Jizhou Zhang, Tingfa Xu Tingfa Xu, Xing Wang Xing Wang, Sining Chen Sining Chen, and Guoqiang Ni Guoqiang Ni. "Fast gradational reconstruction for Fourier ptychographic microscopy." Chinese Optics Letters 15, no. 11 (2017): 111702. http://dx.doi.org/10.3788/col201715.111702.

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2

Ou, Xiaoze, Jaebum Chung, Roarke Horstmeyer, and Changhuei Yang. "Aperture scanning Fourier ptychographic microscopy." Biomedical Optics Express 7, no. 8 (July 29, 2016): 3140. http://dx.doi.org/10.1364/boe.7.003140.

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3

Wang, Lin, Qihao Song, Hongbo Zhang, Caojin Yuan, and Ting-Chung Poon. "Optical scanning Fourier ptychographic microscopy." Applied Optics 60, no. 4 (November 30, 2020): A243. http://dx.doi.org/10.1364/ao.402644.

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4

Loetgering, Lars, Tomas Aidukas, Kevin C. Zhou, Felix Wechsler, and Roarke Horstmeyer. "Fourier Ptychography Part II: Phase Retrieval and High-Resolution Image Formation." Microscopy Today 30, no. 5 (September 2022): 36–39. http://dx.doi.org/10.1017/s1551929522001055.

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Анотація:

Abstract:This article is the second within a three-part series on Fourier ptychography, which is a computational microscopy technique for high-resolution, large field-of-view imaging. While the first article laid out the basics of Fourier ptychography, this second part sheds light on its algorithmic ingredients. We present a non-technical discussion of phase retrieval, which allows for the synthesis of high-resolution images from a sequence of low-resolution raw data. Fourier ptychographic phase retrieval can be carried out on standard, widefield microscopy platforms with the simple addition of a low-cost LED array, thus offering a convenient alternative to other phase-sensitive techniques that require more elaborate hardware such as differential interference contrast and digital holography.

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5

Zhang, Yongbing, Weixin Jiang, Lei Tian, Laura Waller, and Qionghai Dai. "Self-learning based Fourier ptychographic microscopy." Optics Express 23, no. 14 (July 8, 2015): 18471. http://dx.doi.org/10.1364/oe.23.018471.

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6

Liu, Qiulan, Yue Fang, Renjie Zhou, Peng Xiu, Cuifang Kuang, and Xu Liu. "Surface wave illumination Fourier ptychographic microscopy." Optics Letters 41, no. 22 (November 15, 2016): 5373. http://dx.doi.org/10.1364/ol.41.005373.

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7

Zhou, You, Jiamin Wu, Zichao Bian, Jinli Suo, Guoan Zheng, and Qionghai Dai. "Fourier ptychographic microscopy using wavelength multiplexing." Journal of Biomedical Optics 22, no. 6 (June 14, 2017): 066006. http://dx.doi.org/10.1117/1.jbo.22.6.066006.

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8

Horstmeyer, Roarke, Guoan Zheng, Xiaoze Ou, and Changhuei Yang. "Modeling Extensions of Fourier Ptychographic Microscopy." Microscopy and Microanalysis 20, S3 (August 2014): 370–71. http://dx.doi.org/10.1017/s1431927614003572.

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9

Xiu, Peng, Youhua Chen, Cuifang Kuang, Yue Fang, Yifan Wang, Jiannan Fan, Yingke Xu, and Xu Liu. "Structured illumination fluorescence Fourier ptychographic microscopy." Optics Communications 381 (December 2016): 100–106. http://dx.doi.org/10.1016/j.optcom.2016.06.075.

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10

Huang, Kaicheng, Wangwei Hui, Qing Ye, Senlin Jin, Hongyang Zhao, Qiushuai Shi, Jianguo Tian, and Wenyuan Zhou. "Compressed-sampling-based Fourier ptychographic microscopy." Optics Communications 452 (December 2019): 18–24. http://dx.doi.org/10.1016/j.optcom.2019.07.009.

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11

Pan, An, Chao Zuo, Yuege Xie, Ming Lei, and Baoli Yao. "Vignetting effect in Fourier ptychographic microscopy." Optics and Lasers in Engineering 120 (September 2019): 40–48. http://dx.doi.org/10.1016/j.optlaseng.2019.02.015.

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12

Zhang Jinhua, 张瑾华, 张继洲 Zhang Jizhou, 李佳男 Li Jianan, 李杰 Li Jie, 陈毅文 Chen Yiwen, 汪心 Wang Xin, 王舒珊 Wang Shushan та 许廷发 Xu Tingfa. "基于叠层衍射成像的傅里叶叠层显微像差校正方法". Acta Optica Sinica 41, № 10 (2021): 1011001. http://dx.doi.org/10.3788/aos202141.1011001.

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13

Zhang, Peiwei, Jufeng Zhao, Binbin Lin, Xiaohui Wu, and Guangmang Cui. "Hyperspectral microscopy imaging based on Fourier ptychographic microscopy." Journal of Optics 24, no. 5 (March 29, 2022): 055301. http://dx.doi.org/10.1088/2040-8986/ac57b3.

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Анотація:

Abstract Hyperspectral resolution, high spatial resolution, and a wide field of view (FOV) are the targets of optical spectral microscopy imaging. However, hyperspectral microscopy imaging technology cannot provide a wide FOV and a high spatial resolution at the same time. Fourier ptychographic microscopy (FPM) is a novel microscopy imaging technique that uses LEDs at varying angles to capture a series of low-spatial-resolution images that are used to recover images that have both high spatial resolution and a wide FOV. Since FPM cannot obtain the spectral resolution of the sample, in this paper, an efficient strategy based on the FPM system is proposed for the reconstruction of hyperspectral images. First, the traditional FPM setup is optimized, with a new experimental setup based on halogen lamp illumination and a narrow band-pass filter to capture a series of low-spatial-resolution images at different wavelengths. Second, a new algorithm, combining hyperspectral resolution imaging using interpolation compensation and a phase retrieval algorithm, is proposed to reconstruct high-spatial-resolution, wide FOV, and hyperspectral resolution images. Finally, we verified the feasibility and effectiveness of our experimental setup and algorithm by both simulation and experiment. The results show that our method can not only reconstruct high-spatial-resolution and wide FOV images, but also has a spectral resolution of 5 nm.

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14

Carlsen, Mads, Trygve M. Ræder, Can Yildirim, Raquel Rodriguez-Lamas, Carsten Detlefs, and Hugh Simons. "Fourier ptychographic dark field x-ray microscopy." Optics Express 30, no. 2 (January 13, 2022): 2949. http://dx.doi.org/10.1364/oe.447657.

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15

Sun Jiasong, 孙佳嵩, 张玉珍 Zhang Yuzhen, 陈钱 Chen Qian, and 左超 Zuo Chao. "Fourier Ptychographic Microscopy: Theory, Advances, and Applications." Acta Optica Sinica 36, no. 10 (2016): 1011005. http://dx.doi.org/10.3788/aos201636.1011005.

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16

Ou, Xiaoze, Roarke Horstmeyer, Changhuei Yang, and Guoan Zheng. "Quantitative phase imaging via Fourier ptychographic microscopy." Optics Letters 38, no. 22 (November 14, 2013): 4845. http://dx.doi.org/10.1364/ol.38.004845.

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17

Zhang, Yongbing, Weixin Jiang, and Qionghai Dai. "Nonlinear optimization approach for Fourier ptychographic microscopy." Optics Express 23, no. 26 (December 22, 2015): 33822. http://dx.doi.org/10.1364/oe.23.033822.

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18

Zhu, Youqiang, Minglu Sun, Xiong Chen, Hao Li, Quanquan Mu, Dayu Li, and Li Xuan. "Single full-FOV reconstruction Fourier ptychographic microscopy." Biomedical Optics Express 11, no. 12 (November 16, 2020): 7175. http://dx.doi.org/10.1364/boe.409952.

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19

Yang Jiaqi, 杨佳琪, 马. 骁. Ma Xiao, 林锦新 Lin Jinxin, and 钟金钢 Zhong Jingang. "Intensity Correction Research for Fourier Ptychographic Microscopy." Laser & Optoelectronics Progress 54, no. 3 (2017): 031101. http://dx.doi.org/10.3788/lop54.031101.

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20

Pan, An, Yan Zhang, Tianyu Zhao, Zhaojun Wang, Dan Dan, Ming Lei, and Baoli Yao. "System calibration method for Fourier ptychographic microscopy." Journal of Biomedical Optics 22, no. 09 (September 12, 2017): 1. http://dx.doi.org/10.1117/1.jbo.22.9.096005.

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21

Fan, Yao, Jiasong Sun, Qian Chen, Mingqun Wang, and Chao Zuo. "Adaptive denoising method for Fourier ptychographic microscopy." Optics Communications 404 (December 2017): 23–31. http://dx.doi.org/10.1016/j.optcom.2017.05.026.

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22

Zheng, Guoan, Roarke Horstmeyer, and Changhuei Yang. "Wide-field, high-resolution Fourier ptychographic microscopy." Nature Photonics 7, no. 9 (July 28, 2013): 739–45. http://dx.doi.org/10.1038/nphoton.2013.187.

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23

Wang, Xiaoli, Yan Piao, Yuanshang Jin, Jie Li, Zechuan Lin, Jie Cui, and Tingfa Xu. "Fourier Ptychographic Reconstruction Method of Self-Training Physical Model." Applied Sciences 13, no. 6 (March 11, 2023): 3590. http://dx.doi.org/10.3390/app13063590.

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Анотація:

Fourier ptychographic microscopy is a new microscopic computational imaging technology. A series of low-resolution intensity images are collected by a Fourier ptychographic microscopy system, and high-resolution intensity and phase images are reconstructed from the collected low-resolution images by a reconstruction algorithm. It is a kind of microscopy that can achieve both a large field of view and high resolution. Here in this article, a Fourier ptychographic reconstruction method applied to a self-training physical model is proposed. The SwinIR network in the field of super-resolution is introduced into the reconstruction method for the first time. The input of the SwinIR physical model is modified to a two-channel input, and a data set is established to train the network. Finally, the results of high-quality Fourier stack microscopic reconstruction are realized. The SwinIR network is used as the physical model, and the network hyperparameters and processes such as the loss function and optimizer of the custom network are reconstructed. The experimental results show that by using multiple different types of data sets, the two evaluation index values of the proposed method perform best, and the image reconstruction quality is the best after model training. Two different evaluation indexes are used to quantitatively analyze the reconstruction results through numerical results. The reconstruction results of the fine-tuning data set with some real captured images are qualitatively analyzed from the visual effect. The results show that the proposed method is effective, the network model is stable and feasible, the image reconstruction is realized in a short time, and the reconstruction effect is good.

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24

Chen Yican, 陈奕灿, 吴霞 Wu Xia, 罗志 Luo Zhi, 杨恢东 Yang Huidong, and 黄波 Huang Bo. "Fourier Ptychographic Microscopy Reconstruction Based on Deep Learning." Laser & Optoelectronics Progress 57, no. 22 (2020): 221106. http://dx.doi.org/10.3788/lop57.221106.

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25

Zheng, Guoan, Xiaoze Ou, Roarke Horstmeyer, Jaebum Chung, and Changhuei Yang. "Fourier Ptychographic Microscopy: A Gigapixel Superscope for Biomedicine." Optics and Photonics News 25, no. 4 (April 1, 2014): 26. http://dx.doi.org/10.1364/opn.25.4.000026.

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26

Zhang, Yan, An Pan, and Ming Lei. "Data preprocessing methods for robust Fourier ptychographic microscopy." Optical Engineering 56, no. 12 (December 15, 2017): 1. http://dx.doi.org/10.1117/1.oe.56.12.123107.

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27

Lee, Hwihyeong, Byong Hyuk Chon, and Hee Kyung Ahn. "Reflective Fourier ptychographic microscopy using a parabolic mirror." Optics Express 27, no. 23 (November 7, 2019): 34382. http://dx.doi.org/10.1364/oe.27.034382.

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28

Ou, Xiaoze, Guoan Zheng, and Changhuei Yang. "Embedded pupil function recovery for Fourier ptychographic microscopy." Optics Express 22, no. 5 (February 24, 2014): 4960. http://dx.doi.org/10.1364/oe.22.004960.

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29

Hou, Lexin, Hexin Wang, Markus Sticker, Lars Stoppe, Junhua Wang, and Min Xu. "Adaptive background interference removal for Fourier ptychographic microscopy." Applied Optics 57, no. 7 (February 26, 2018): 1575. http://dx.doi.org/10.1364/ao.57.001575.

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30

Horstmeyer, Roarke, and Changhuei Yang. "A phase space model of Fourier ptychographic microscopy." Optics Express 22, no. 1 (January 2, 2014): 338. http://dx.doi.org/10.1364/oe.22.000338.

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31

Konda, Pavan Chandra, Jonathan M. Taylor, and Andrew R. Harvey. "Multi-aperture Fourier ptychographic microscopy, theory and validation." Optics and Lasers in Engineering 138 (March 2021): 106410. http://dx.doi.org/10.1016/j.optlaseng.2020.106410.

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32

Lee, Byounghyo, Jong-young Hong, Dongheon Yoo, Jaebum Cho, Youngmo Jeong, Seokil Moon, and Byoungho Lee. "Single-shot phase retrieval via Fourier ptychographic microscopy." Optica 5, no. 8 (August 8, 2018): 976. http://dx.doi.org/10.1364/optica.5.000976.

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33

Zhang, Jizhou, Tingfa Xu, Jingdan Liu, Sining Chen, and Xing Wang. "Precise Brightfield Localization Alignment for Fourier Ptychographic Microscopy." IEEE Photonics Journal 10, no. 1 (February 2018): 1–13. http://dx.doi.org/10.1109/jphot.2017.2780189.

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34

Ni, Ying-Hui, Si-Yuan Fan, Shu-Yuan Zhang, and Ming-Jie Sun. "Hyperuniform illumination subsampling method for Fourier ptychographic microscopy." Optics and Lasers in Engineering 176 (May 2024): 108106. http://dx.doi.org/10.1016/j.optlaseng.2024.108106.

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35

Xu, Fannuo, Zipei Wu, Chao Tan, Yizheng Liao, Zhiping Wang, Keru Chen, and An Pan. "Fourier Ptychographic Microscopy 10 Years on: A Review." Cells 13, no. 4 (February 10, 2024): 324. http://dx.doi.org/10.3390/cells13040324.

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Анотація:

Fourier ptychographic microscopy (FPM) emerged as a prominent imaging technique in 2013, attracting significant interest due to its remarkable features such as precise phase retrieval, expansive field of view (FOV), and superior resolution. Over the past decade, FPM has become an essential tool in microscopy, with applications in metrology, scientific research, biomedicine, and inspection. This achievement arises from its ability to effectively address the persistent challenge of achieving a trade-off between FOV and resolution in imaging systems. It has a wide range of applications, including label-free imaging, drug screening, and digital pathology. In this comprehensive review, we present a concise overview of the fundamental principles of FPM and compare it with similar imaging techniques. In addition, we present a study on achieving colorization of restored photographs and enhancing the speed of FPM. Subsequently, we showcase several FPM applications utilizing the previously described technologies, with a specific focus on digital pathology, drug screening, and three-dimensional imaging. We thoroughly examine the benefits and challenges associated with integrating deep learning and FPM. To summarize, we express our own viewpoints on the technological progress of FPM and explore prospective avenues for its future developments.

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36

Wang, Xiaoli, Yan Piao, Jie Li, and Jinyang Yu. "Fourier Ptychographic Microscopy Reconstruction Method Based on Residual Transfer Networks." Journal of Physics: Conference Series 2400, no. 1 (December 1, 2022): 012015. http://dx.doi.org/10.1088/1742-6596/2400/1/012015.

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Анотація:

Abstract Fourier ptychographic microscopy reconstruction mostly adopts the traditional alternating iterative phase recovery method and optimization method, which has high computational complexity, high redundancy of image acquisition data, low reconstruction quality and high time consumption. In this paper, the model of residual transfer networks based on Resnet152 is proposed for Fourier ptychographic microscopy reconstruction, the learning process of deep convolution neural network is introduced, and the image reconstruction method based on deep learning realizes the end-to-end reconstruction of low-resolution images to high-resolution images. Through comparative experiments and analysis, the residual network can overcome the gradient explosion, make the feature information more complete and efficient, and the incremental up-sampling reconstruction network has higher image quality, lower computational complexity and shorter running time.

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37

Alotaibi, Maged F. "Reconstruction of Talbot self-image using Fourier ptychographic microscopy." Alexandria Engineering Journal 61, no. 12 (December 2022): 12151–57. http://dx.doi.org/10.1016/j.aej.2022.06.016.

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38

Wang, Aiye, Zhuoqun Zhang, Siqi Wang, An Pan, Caiwen Ma, and Baoli Yao. "Fourier Ptychographic Microscopy via Alternating Direction Method of Multipliers." Cells 11, no. 9 (April 30, 2022): 1512. http://dx.doi.org/10.3390/cells11091512.

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Анотація:

Fourier ptychographic microscopy (FPM) has risen as a promising computational imaging technique that breaks the trade-off between high resolution and large field of view (FOV). Its reconstruction is normally formulated as a blind phase retrieval problem, where both the object and probe have to be recovered from phaseless measured data. However, the stability and reconstruction quality may dramatically deteriorate in the presence of noise interference. Herein, we utilized the concept of alternating direction method of multipliers (ADMM) to solve this problem (termed ADMM-FPM) by breaking it into multiple subproblems, each of which may be easier to deal with. We compared its performance against existing algorithms in both simulated and practical FPM platform. It is found that ADMM-FPM method belongs to a global optimization algorithm with a high degree of parallelism and thus results in a more stable and robust phase recovery under noisy conditions. We anticipate that ADMM will rekindle interest in FPM as more modifications and innovations are implemented in the future.

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39

Tao, Xiao, Jinlei Zhang, Peng Sun, Chang Wang, Chenning Tao, Rengmao Wu, and Zhenrong Zheng. "Phase-coded speckle illumination for laser Fourier ptychographic microscopy." Optics Communications 498 (November 2021): 127199. http://dx.doi.org/10.1016/j.optcom.2021.127199.

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40

Kuang, Cuifang, Ye Ma, Renjie Zhou, Justin Lee, George Barbastathis, Ramachandra R. Dasari, Zahid Yaqoob, and Peter T. C. So. "Digital micromirror device-based laser-illumination Fourier ptychographic microscopy." Optics Express 23, no. 21 (October 5, 2015): 26999. http://dx.doi.org/10.1364/oe.23.026999.

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41

Ou, Xiaoze, Guoan Zheng, and Changhuei Yang. "Embedded pupil function recovery for Fourier ptychographic microscopy: erratum." Optics Express 23, no. 26 (December 14, 2015): 33027. http://dx.doi.org/10.1364/oe.23.033027.

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42

Zheng, Guoan, Roarke Horstmeyer, and Changhuei Yang. "Erratum: Corrigendum: Wide-field, high-resolution Fourier ptychographic microscopy." Nature Photonics 9, no. 9 (August 27, 2015): 621. http://dx.doi.org/10.1038/nphoton.2015.148.

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43

Sun, Jiasong, Qian Chen, Yuzhen Zhang, and Chao Zuo. "Efficient positional misalignment correction method for Fourier ptychographic microscopy." Biomedical Optics Express 7, no. 4 (March 17, 2016): 1336. http://dx.doi.org/10.1364/boe.7.001336.

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44

Chung, Jaebum, Hangwen Lu, Xiaoze Ou, Haojiang Zhou, and Changhuei Yang. "Wide-field Fourier ptychographic microscopy using laser illumination source." Biomedical Optics Express 7, no. 11 (October 31, 2016): 4787. http://dx.doi.org/10.1364/boe.7.004787.

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45

Zhang, Jizhou, Tingfa Xu, Ziyi Shen, Yifan Qiao, and Yizhou Zhang. "Fourier ptychographic microscopy reconstruction with multiscale deep residual network." Optics Express 27, no. 6 (March 11, 2019): 8612. http://dx.doi.org/10.1364/oe.27.008612.

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46

Zhang, Jizhou, Tingfa Xu, Sining Chen, and Xing Wang. "Efficient Colorful Fourier Ptychographic Microscopy Reconstruction With Wavelet Fusion." IEEE Access 6 (2018): 31729–39. http://dx.doi.org/10.1109/access.2018.2841854.

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47

Wang, Xing, Tingfa Xu, Jizhou Zhang, Sining Chen, and Yizhou Zhang. "SO-YOLO Based WBC Detection With Fourier Ptychographic Microscopy." IEEE Access 6 (2018): 51566–76. http://dx.doi.org/10.1109/access.2018.2865541.

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48

Liu, Qiulan, Cuifang Kuang, Yue Fang, Peng Xiu, Yicheng Li, Ruixin Wen, and Xu Liu. "Effect of spatial spectrum overlap on Fourier ptychographic microscopy." Journal of Innovative Optical Health Sciences 10, no. 02 (March 2017): 1641004. http://dx.doi.org/10.1142/s1793545816410042.

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Анотація:

Fourier ptychographic microscopy (FPM) is a newly developed imaging technique which stands out by virtue of its high-resolution and wide FOV. It improves a microscope’s imaging performance beyond the diffraction limit of the employed optical components by illuminating the sample with oblique waves of different incident angles, similar to the concept of synthetic aperture. We propose to use an objective lens with high-NA to generate oblique illuminating waves in FPM. We demonstrate utilizing an objective lens with higher NA to illuminate the sample leads to better resolution by simulations, in which a resolution of 0.28[Formula: see text][Formula: see text]m is achieved by using a high-NA illuminating objective lens (NA[Formula: see text][Formula: see text]) and a low-NA collecting objective lens (NA[Formula: see text][Formula: see text]) in coherent imaging ([Formula: see text][Formula: see text]nm). We then deeply study FPM’s exact relevance of convergence speed to spatial spectrum overlap in frequency domain. The simulation results show that an overlap of about 60% is the optimal choice to acquire a high-quality recovery (520*520 pixels) with about 2 min’s computing time. In addition, we testify the robustness of the algorithm of FPM to additive noises and its suitability for phase objects, which further proves FPM’s potential application in biomedical imaging.

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