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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:123下載:0
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  • 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

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