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基于三维卷积神经网络的彩色傅里叶叠层显微术

Color Fourier Ptychography Microscopy Using Three-Dimensional Convolutional Neural Network

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摘要

包含多波长信息的低分辨(LR)灰度图难以被完全解复用,根据LR图像信息重建出的彩色高分辨(HR)图像容易出现通道串扰的现象。为重建不受通道串扰干扰的彩色HR图像,提出一种基于三维卷积神经网络(CNN)的彩色HR图像重建算法。采用主成分分析法提取单色HR图像和彩色LR图像的结构信息,然后基于结构信息训练CNN来建立单色HR图像和彩色LR图像之间的映射关系,最后生成彩色HR图像。实验结果表明,所提算法可以获得不受通道串扰影响、色彩不失真的彩色HR图像。定量评价指标方均根误差小于0.1,结构相似性参数大于0.9。

Abstract

Low-resolution (LR) grayscale images with multi-wavelength information are difficult to fully demultiplex. High-resolution (HR) colored images reconstructed from LR images are prone to channel crosstalk. To reconstruct HR colored images that are not prone to channel crosstalk, we propose an HR colored image reconstruction algorithm based on a three-dimensional convolutional neural network (CNN). The principal component analysis method is used to extract structural information from HR monochromatic images and LR colored images, and then the CNN is trained based on the structural information to establish a mapping relationship between the HR monochromatic image and LR colored image. Consequently, a HR colored image is generated. The experimental results show that the proposed algorithm can obtain HR colored images without channel crosstalk and color distortion. The quantitative evaluation indexs show that the root mean square error and structural similarity parameter are less than 0.1 and greater than 0.9, respectively.

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中图分类号:O438.2

DOI:10.3788/AOS202040.2011001

所属栏目:成像系统

基金项目:国家自然科学基金、天津市科技支撑重点项目;

收稿日期:2020-05-18

修改稿日期:2020-06-28

网络出版日期:2020-10-01

作者单位    点击查看

张慕阳:南开大学现代光学研究所, 天津 300350
梁艳梅:南开大学现代光学研究所, 天津 300350

联系人作者:梁艳梅(ymliang@nankai.edu.cn)

备注:国家自然科学基金、天津市科技支撑重点项目;

【1】Zheng G A, Horstmeyer R, Yang C. Wide-field, high-resolution Fourier ptychographic microscopy [J]. Nature Photonics. 2013, 7(9): 739-745.Zheng G A, Horstmeyer R, Yang C. Wide-field, high-resolution Fourier ptychographic microscopy [J]. Nature Photonics. 2013, 7(9): 739-745.

【2】Zheng G A. Breakthroughs in photonics 2013: Fourier ptychographic imaging [J]. IEEE Photonics Journal. 2014, 6(2): 0701207.Zheng G A. Breakthroughs in photonics 2013: Fourier ptychographic imaging [J]. IEEE Photonics Journal. 2014, 6(2): 0701207.

【3】Ou X Z, Horstmeyer R, Zheng G A, et al. High numerical aperture Fourier ptychography: principle, implementation and characterization [J]. Optics Express. 2015, 23(3): 3472-3491.

【4】Liu Q L, Fang Y, Zhou R J, et al. Surface wave illumination Fourier ptychographic microscopy [J]. Optics Letters. 2016, 41(22): 5373-5376.

【5】Sun J S, Zuo C, Zhang L, et al. Resolution-enhanced Fourier ptychographic microscopy based on high-numerical-aperture illuminations [J]. Scientific Reports. 2017, 7(1): 1187.

【6】Skinner-Ramos S, Farooq H, Bernussi A A, et al. Fourier plane imaging and illumination-direction-multiplexing using a rotating diffracting element for Fourier ptychographic microscopy [J]. Optics Communications. 2018, 427: 231-237.

【7】Li Z H, Zhang J Q, Wang X R, et al. High resolution integral holography using Fourier ptychographic approach [J]. Optics Express. 2014, 22(26): 31935-31947.

【8】Ou X Z, Zheng G A, Yang C. Embedded pupil function recovery for Fourier ptychographic microscopy [J]. Optics Express. 2014, 22(5): 4960-4972.

【9】Tian L, Li X, Ramchandran K, et al. Multiplexed coded illumination for Fourier ptychography with an LED array microscope [J]. Biomedical Optics Express. 2014, 5(7): 2376-2389.

【10】Chung J, Ou X Z, Kulkarni R P, et al. Counting white blood cells from a blood smear using Fourier ptychographic microscopy [J]. PLoS One. 2015, 10(7): e0133489.

【11】Dong S Y, Liao J, Guo K K, et al. Resolution doubling with a reduced number of image acquisitions [J]. Biomedical Optics Express. 2015, 6(8): 2946-2952.

【12】Zhang Y B, Cui Z, Zhang J, et al. Group-based sparse representation for Fourier ptychography microscopy [J]. Optics Communications. 2017, 404: 55-61.

【13】Song P M, Jiang S W, Zhang H, et al. Full-field Fourier ptychography (FFP): spatially varying pupil modeling and its application for rapid field-dependent aberration metrology [J]. APL Photonics. 2019, 4(5): 050802.

【14】Horstmeyer R, Ou X Z, Zheng G A, et al. Digital pathology with Fourier ptychography [J]. Computerized Medical Imaging and Graphics. 2015, 42: 38-43.

【15】Alotaibi M, Skinner-Ramos S, Farooq H, et al. Imaging photonic crystals using hemispherical digital condensers and phase-recovery techniques [J]. Applied Optics. 2018, 57(14): 3756-3760.

【16】Pan A, Wen K, Yao B L. Linear space-variant optical cryptosystem via Fourier ptychography [J]. Optics Letters. 2019, 44(8): 2032-2035.

【17】Bian L H, Suo J L, Zheng G A, et al. Fourier ptychographic reconstruction using Wirtinger flow optimization [J]. Optics Express. 2015, 23(4): 4856-4866.

【18】Zhang M Y, Zhang L L, Yang D, et al. Symmetrical illumination based extending depth of field in Fourier ptychographic microscopy [J]. Optics Express. 2019, 27(3): 3583-3597.

【19】Zhang L L, Tang L J, Zhang M Y, et al. Symmetric illumination in Fourier ptychography [J]. Acta Physica Sinica. 2017, 66(22): 224201.
张雷雷, 唐立金, 张慕阳, 等. 对称照明在傅里叶叠层成像中的应用 [J]. 物理学报. 2017, 66(22): 224201.

【20】Tian L, Liu Z J, Yeh L H, et al. Computational illumination for high-speed in vitro Fourier ptychographic microscopy [J]. Optica. 2015, 2(10): 904.

【21】Zhou A, Chen N, Wang H C, et al. Analysis of Fourier ptychographic microscopy with half of the captured images [J]. Journal of Optics. 2018, 20(9): 095701.

【22】Zhang Y B, Song P M, Dai Q H. Fourier ptychographic microscopy using a generalized Anscombe transform approximation of the mixed Poisson-Gaussian likelihood [J]. Optics Express. 2017, 25(1): 168-179.

【23】Bian L H, Suo J L, Chung J, et al. Fourier ptychographic reconstruction using Poisson maximum likelihood and truncated Wirtinger gradient [J]. Scientific Reports. 2016, 6: 27384.

【24】Zhou Y, Wu J M, Bian Z C, et al. Fourier ptychographic microscopy using wavelength multiplexing [J]. Journal of Biomedical Optics. 2017, 22(6): 066006.

【25】Dong S Y, Shiradkar R, Nanda P, et al. Spectral multiplexing and coherent-state decomposition in Fourier ptychographic imaging [J]. Biomedical Optics Express. 2014, 5(6): 1757-1767.

【26】Wang M Q, Zhang Y Z, Chen Q, et al. A color-corrected strategy for information multiplexed Fourier ptychographic imaging [J]. Optics Communications. 2017, 405: 406-411.

【27】Zhang J Z, Xu T F, Chen S N, et al. Efficient colorful Fourier ptychographic microscopy reconstruction with wavelet fusion [J]. IEEE Access. 2018, 6: 31729-31739.

【28】Litjens G, Kooi T, Bejnordi B E, et al. A survey on deep learning in medical image analysis [J]. Medical Image Analysis. 2017, 42: 60-88.

【29】Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology [J]. Nature Reviews Cancer. 2018, 18(8): 500-510.

【30】Tajbakhsh N, Shin J Y, Gurudu S R, et al. Convolutional neural networks for medical image analysis: full training or fine tuning? [J]. IEEE Transactions on Medical Imaging. 2016, 35(5): 1299-1312.

【31】Lin H N, Shi Z W, Zou Z X. Fully convolutional network with task partitioning for inshore ship detection in optical remote sensing images [J]. IEEE Geoscience and Remote Sensing Letters. 2017, 14(10): 1665-1669.

【32】Palsson F, Sveinsson J R, Ulfarsson M O. Multispectral and hyperspectral image fusion using a 3-D-convolutional neural network [J]. IEEE Geoscience and Remote Sensing Letters. 2017, 14(5): 639-643.

【33】Zhang R Q, Yao J, Zhang K, et al. S-CNN ship detection from high-resolution remote sensing images . [C]∥ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, July 12-19, 2016, Prague, Czech Republic. Gottingen: Copernicus Publication. 2016, 423-430.

【34】Sainath T N, Weiss R J, Wilson K W, et al. Multichannel signal processing with deep neural networks for automatic speech recognition [J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2017, 25(5): 965-979.

【35】Qian Y M, Bi M X, Tan T, et al. Very deep convolutional neural networks for noise robust speech recognition [J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2016, 24(12): 2263-2276.

【36】Cheng Y F, Strachan M, Weiss Z, et al. Illumination pattern design with deep learning for single-shot Fourier ptychographic microscopy [J]. Optics Express. 2019, 27(2): 644-656.

【37】Jiang S W, Guo K K, Liao J, et al. Solving Fourier ptychographic imaging problems via neural network modeling and TensorFlow [J]. Biomedical Optics Express. 2018, 9(7): 3306-3319.

【38】Sun M L, Chen X, Zhu Y Q, et al. Neural network model combined with pupil recovery for Fourier ptychographic microscopy [J]. Optics Express. 2019, 27(17): 24161-24174.

【39】Nguyen T, Xue Y J, Li Y Z, et al. Deep learning approach for Fourier ptychography microscopy [J]. Optics Express. 2018, 26(20): 26470-26484.

【40】Robey A, Ganapati V. Optimal physical preprocessing for example-based super-resolution [J]. Optics Express. 2018, 26(24): 31333-31350.

【41】Gonzalez R C, Woods R E. Digital image processing [M]. 3rd ed. USA: Pearson Education. 2008, 842-852.

【42】Hotelling H. Analysis of a complex of statistical variables into principal components [J]. Journal of Educational Psychology. 1933, 24(6): 498-520.

【43】Kingma D P. -01-30)[2020-05-17] . https:∥arxiv. 2017, org/abs/1412: 6980.

引用该论文

Zhang Muyang,Liang Yanmei. Color Fourier Ptychography Microscopy Using Three-Dimensional Convolutional Neural Network[J]. Acta Optica Sinica, 2020, 40(20): 2011001

张慕阳,梁艳梅. 基于三维卷积神经网络的彩色傅里叶叠层显微术[J]. 光学学报, 2020, 40(20): 2011001

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