簡易檢索 / 詳目顯示

研究生: 高揚傑
Gao, Yang-Jie
論文名稱: 運用波前修正於數位全像造影及其深度學習致動粒子偵測之研究
Studies on Wavefront Correction for Digital Holographic Imaging and Its Application in Deep Learning-enabled Particle Detection
指導教授: 鄭超仁
Cheng, Chau-Jern
學位類別: 碩士
Master
系所名稱: 光電工程研究所
Graduate Institute of Electro-Optical Engineering
論文出版年: 2020
畢業學年度: 108
語文別: 中文
論文頁數: 83
中文關鍵詞: 全像術數位全像術波前修正Zernike多項式深度學習卷積神經網路粒子偵測
英文關鍵詞: Holography, Digital Holography, Wavefront Correction, Zernike Polynomials, Deep Learning, Convolution Neural Network, Particle Detection
DOI URL: http://doi.org/10.6345/NTNU202001466
論文種類: 學術論文
相關次數: 點閱:157下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 本論文主要探討利用數位全像式的資料及波前修正技術於深度學習以影像辨識上的優勢,以達到三維粒子偵測之目的。在數位全像造影中,本文探討波前像差對於樣品資訊的影響及修正方法,以得到正確的物體資訊,同時運用數位全像資料擴增方法,來提升數據集的多樣性。而運用上述方法即可透過數位全像術取得粒子的波前繞射資訊,再運用深度學習於物件偵測的技術,藉由調整模型架構及參數,來使樣品偵測能力及辨識能力達到最大準確度,來進行三維空間位置定位及尺寸分類,以利未來透過數位全像顯微造影系統擷取其他樣品的光場資訊進行定位,增加未來應用的潛力。

    This thesis mainly discusses the advantages of using digital holographic data and wavefront correction technology in deep learning and image recognition for 3D particle detection. This article discusses the influence of wavefront aberration on sample information and correction methods to obtain correct object information. To diversify the data set, digital holographic data amplification methods are used. Using digital holography, the wavefront diffraction information of the particles can be obtained. Using this diversified data set, deep learning technology in object detection is applied. The model structure and parameters are adjusted to maximize the sample detection and identification capabilities. Accuracy is used for three-dimensional spatial location positioning and size classification. For future purpose, the light field information of other samples can also be captured through the digital holographic microscopy system, increasing the potential for future applications.

    論文摘要 I ABSTRACT II 目錄 III 圖目錄 VI 表目錄 X 第一章 緒論 1 1.1 數位全像造影之技術發展與應用現況 1 1.2 文獻回顧與分析 7 1.2.1 數位全像術 7 1.2.2 波前修正 10 1.2.3 深度學習與數位全像術的關聯性 13 1.2.4 三維空間粒子偵測 14 1.3 研究目的與動機 18 1.4 論文架構 19 第二章 數位全像原理 20 2.1 數位全像記錄與重建 20 2.2 光場繞射 22 2.3 數位全像顯微造影系統 24 2.3.1 光學限制 26 2.3.2 數位限制 26 第三章 波前修正方法 28 3.1 數位全像顯微造影系統之波前像差 28 3.2 用Zernike多項式之波前像差分析 32 3.2.1 矩形Zernike多項式之方法 32 3.2.2 Zernike多項式波前擬合方法 35 3.3 使用Zernike多項式於數位全像波前像差修正 36 第四章 基於數位全像之深度學習致動粒子偵測方法 40 4.1 深度學習原理 40 4.1.1 深度學習之相關技術 42 4.1.2 卷積神經網路 50 4.2 數位全像資料擴增 51 4.3 深度學習應用於數位全像粒子三維偵測 53 4.3.1 粒子之繞射特性 53 4.3.2 粒子繞射重建方法 54 4.3.3 深度學習方法 55 第五章 光學實現數位全像式深度學習致動粒子偵測 59 5.1 數位全像式資料庫建置 60 5.1.1 光學系統架構 60 5.1.2 實驗樣品收集與分析 61 5.1.3 實驗樣品之標記程式 63 5.1.4 數位全像式資料重建及分析 64 5.2 深度學習致動粒子偵測 65 5.2.1 用於橫向位置檢測之深度學習模型 66 5.2.2 用於縱向位置檢測之深度學習模型 67 5.2.3 用於尺寸檢測之深度學習模型 68 5.3 結果討論與分析 70 5.3.1 電腦系統架構與實驗環境 70 5.3.2 效能評估 71 第六章 結論與未來展望 73 參考文獻 75

    [1] D. Gabor, “A New Microscopy Principle,” Nature 161, 777-778 (1948).
    [2] E. N. Leith and J. Upatnieks, “Reconstructed Wavefronts and Communication Theory,” J. Opt. Soc. Am. 52, 1123-1130 (1962).
    [3] J. W. Goodman and R. W. Lawrence, “Digital image formation from electronically detected holograms,” Appl. Phys. Lett. 11, 77 (1967).
    [4] T. Zhang and I. Yamaguchi, “Phase-shifting digital holography,” Opt. Lett. 22, 1268-1270 (1997).
    [5] T. Zhang and I. Yamaguchi, “Three-dimensional microscopy with phase-shifting digital holography,” Opt. Lett. 23, 1221-1223 (1998).
    [6] G. Pedrini and H. J. Tiziani, “Short-coherence digital microscopy by use of a lensless holographic imaging system,” Appl. Opt. 44, 3977-3984 (2005).
    [7] B. Vinoth, X. J. Lai, Y. C. Lin, H. Y. Tu and C. J. Cheng, “Integrated dual-tomography for refractive index analysis of free-floating single living cell with isotropic superresolution,” Sci. Rep. 8, 5943 (2018).
    [8] C. Cortes and V. Vapnik, “Support-Vector Networks,” Mach. Learning 20, 273–297 (1995).
    [9] H. Hotelling, “Analysis of a complex of statistical variables into principal components,” J. Educ. Psychol. 24, 417-441, 498-520. (1933).
    [10] S. Warren, McCulloch and Walter Pitts “A logical calculus of the ideas immanent in nervous activity” Bulletin of Mothemnticnl Biology 5, 115-133 (1943).
    [11] F. Rosenblatt, “The perceptron a probabilistic model for information storage and organization in the brain,” Psychol. Rev. 65, 386-408 (1958).
    [12] J. Dean. Trends and Developments in Deep Learning Research. Retrieved from https://www.slideshare.net/AIFrontiers/jeff-dean-trends-and-developments-in-deep-learning-research (2017).
    [13] A Canziani, A Paszke and E Culurciello, “An Analysis of Deep Neural Network Models for Practical Applications,” arXiv preprint (2016).
    [14] L. Denis, C. Fournier, T. Fournel, C. Ducottet and D. Jeulin, “Direct extraction of the mean particle size from a digital hologram,” Appl. Opt. 45, 944-952 (2006).
    [15] J. Garcia-Sucerquia, W. Xu, S. K. Jericho, P. Klages, M. H. Jericho and H. J. Kreuzer, “Digital in-line holographic microscopy,” Appl. Opt. 45, 836-850 (2006).
    [16] Et. Cuche, P. Marquet, and C. Depeursinge “Simultaneous amplitude-contrast and quantitative phase-contrast microscopy by numerical reconstruction of Fresnel off-axis holograms,” Appl. Opt. 38, 6994-7001 (1999).
    [17] Y. Deng, C. H. Huang, B. Vinoth, D. Chu, X. J. Lai and C. J. Cheng, “A compact synthetic aperture digital holographic microscope with mechanical movement-free beam scanning and optimized active aberration compensation for isotropic resolution enhancement,” Opt. Lasers Eng. 134, 106251 (2020).
    [18] C. Zuo, Q. Chen, W. Qu and A. Asundi, “Phase aberration compensation in digital holographic microscopy based on principal component analysis,” Opt. Lett. 38, 1724-1726 (2013)
    [19] Z. Ren, J. Zhao and E. Y. Lam, “Automatic compensation of phase aberrations in digital holographic microscopy based on sparse optimization,” APL Photonics 4, 110808 (2019).
    [20] T. Nguyen, V. Bui, V. Lam, C. B. Raub, L. C. Chang and G. Nehmetallah, “Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection,” Opt. Express 25, 15043-15057 (2017).
    [21] G. Litjens, F. Ciompi, J. M. Wolterink, Bob D de Vos, T. Leiner, J. Teuwen and I. Išgum, “State-of-the-Art Deep Learning in Cardiovascular Image Analysis,” JACC Cardiovasc. Imaging 12, 1549-1565 (2019).
    [22] T. Pitkäaho, A. Manninen and T. J. Naughton, “Focus prediction in digital holographic microscopy using deep convolutional neural networks,” Appl. Opt. 58, A202-A208 (2019).
    [23] T. Shimobaba, T. Kakue and T. Ito, “Convolutional neural network-based regression for depth prediction in digital holography,” IEEE, 1323-1326 (2018).
    [24] Y. Rivenson, Y. Zhang, H. Gunaydin, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light Sci. Appl. 7, e17141 (2018).
    [25] W. Jeon, W. Jeong, K. Son and H. Yang, “Speckle noise reduction for digital holographic images using multi-scale convolutional neural networks,” Opt. Lett. 43, 4240-4243 (2020).
    [26] H. Byeona, T. Gob and S. J. Lee, “Deep learning-based digital in-line holographic microscopy for high resolution with extended field of view,” Opt. Laser Technol. 113, 77-86 (2019.)
    [27] S. W. Paine and J. R. Fienup, “Machine learning for improved image-based wavefront sensing,” Opt. Lett. 43, 1235-1238 (2018).
    [28] T. Shimobaba, T. Takahashi, Y. Yamamoto, Y. Endo, A. Shiraki, T. Nishitsuji, N. Hoshikawa, T. Kakue and T. Ito, “Digital holographic particle volume reconstruction using a deep neural network,” Appl. Opt. 58, 1900-1906 (2019).
    [29] S. J. Lee, G. Y. Yoon and T. Go, “Deep learning-based accurate and rapid tracking of 3D positional information of microparticles using digital holographic microscopy,” Exp. Fluids 60, 170 (2019).
    [30] L. F. Yu and M. K. Kim, “Wavelength-scanning digital interference holography for tomographic three-dimensional imaging by use of the angular spectrum method,” Opt. Lett. 30, 2092–2094 (2005).
    [31] F. Charrière, J. Kühn, T. Colomb, F. Montfort, E. Cuche, Y. Emery, K. Weible, P. Marquet and C. Depeursinge, “Characterization of microlenses by digital holographic microscopy,” Appl. Opt. 45, 829-835 (2006).
    [32] I. Yamaguchi, J. Kato, S. Ohta and J. Mizuno, “Image formation in phase-shifting digital holography and applications to microscopy,” Appl. Opt. 40, 6177-6186 (2001).
    [33] V. N. Mahajan and G. M. Dai, “Orthonormal polynomials in wavefront analysis: analytical solution,” J. Opt. Soc. Am. A. Opt. Image Sci. Vis. 24, 2994-3016 (2007).
    [34] A. M. Turing, “Computing Machinery and Intelligence,” Mind 49, 433-460 (1950).
    [35] G.E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science 313, 504–507 (2006).
    [36] Y. LeCun, L. Bottou, Y. Bengio and P. Haffner. “Gradient-based learning applied to document recognition,” Proc. IEEE (1998).
    [37] L. Xie, T. Ahmad, L. Jin, “A new CNN-based method for multi-directional car license plate detection,” IEEE trans. Intell. Transp. Syst. 19, 507-517 (2018).
    [38] C. J. Cheng, K. C. Chang Chien, and Y. C. Lin, “Digital hologram for data augmentation in learning-based pattern classification,” Opt. Lett. 43, 5419-5422 (2018).
    [39] P. Langehanenberg, B. Kemper, D. Dirksen and G. von Bally, “Autofocusing in digital holographic phase contrast microscopy on pure phase objects for live cell imaging,” Appl. Opt. 47, D176-D182 (2008).
    [40] Krizhevsky, I. Sutskever and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Proc. 25th Int. Conf. Neural Inf. Process. Syst., 1097–11105 (2012).
    [41] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Er-han, V. Vanhoucke and A. Rabinovich, “Going deeper with convolu-tions,” In CVPR (2015).
    [42] O. Ronneberger, P. Fischer and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” In MIC-CAI, 234–241 (2015).
    [43] S. Shao, K. Mallery, S. S. Kumar and Jiarong Hong, “Machine learning holography for 3D particle field imaging,” Opt. Express 28, 2987-2999 (2020).
    [44] S. Shao, K. Mallery and J. Hong, “Machine learning holography for measuring 3D particle distribution,” Chem. Eng. Sci. 225, 115830 (2020).
    [45] S. Ren, K. He, R. Girshick and J. Sun, “Faster R-CNN: Towardsreal-time object detection with region proposal networks,” Proc. 28th Int. Conf. Neural Inf. Process. Syst., 91-99 (2015).
    [46] W. Liu, D. Anguelov, D. Erhan, C. Szegedy and S. Reed, “SSD: Single shot multibox detector,” ECCV (2015).
    [47] J. Redmon, S. Divvala, R. Girshick and A. Farhad, “You only look once: unified, real-time object detection,” CVPR (2016).
    [48] V. Bianco, P. Memmolo, M. Leo, et al., “Strategies for reducing speckle noise in digital holography,” Light Sci. Appl. 7, 48 (2018).

    無法下載圖示 本全文未授權公開
    QR CODE