A Similarity-Guided Spots Sorting Method to Increase the Dynamic Range of a Shack Hartmann Sensor

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姓名

潘淑芳(Phua Soo Fan) 查詢紙本館藏

畢業系所

光電科學與工程學系

論文名稱

(A Similarity-Guided Spots Sorting Method to Increase the Dynamic Range of a Shack Hartmann Sensor)

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摘要(中)

在Shack-Hartmann 波前感應器的資料處理中，正確地把光點分配到各個微透鏡是非常重要的。傳統的分配算法的限制斑點的量轉移到微透鏡的直徑的一半，從而限制了Shack Hartmann感應器的動態範圍。

這項工作提出了擴展的動態範圍的Shack-Hartmann波前感應器的質量引導演算法。該算法提供了高動態範圍，並且準確度不受局部缺陷及光點遺失的影響。

該演算法經由模擬的Shack-Hartmann點和實驗獲得的Shack-Hartmann點做測試，並與現有的演算法相比。我們對模擬結果和實際情況的數據進行了分析，並對它們之間的差異進行了討論。

摘要(英)

Assignment of spots to the correct lenslet is critically important to a Shack Hartmann wavefront sensor. Conventional assignment-algorithm limits the amount of spots shift to be half the lenslet diameter, thereby limiting the dynamic range of the sensor.
This work presents a quality-guided algorithm to extend the dynamic range of a Shack-Hartmann wavefront sensor. The proposed algorithm offers a high dynamic range and excellent robustness.
The algorithm is tested on both simulated Shack-Hartmann spots and experimentally captured Shack-Hartmann spots.. The performance of the proposed algorithm is compared with existing algorithms. Results from simulation and from real-case data are analyzed. Any discrepancies are discussed.

關鍵字(中)

★ 波前量測
★ 波前重建
★ 動態範圍

關鍵字(英)

★ Shack Hartmann
★ Spots Assignment
★ Dynamic Range
★ Spots Sorting
★ Quality Guided

論文目次

Abstract
Acknowledgement
摘要 I
Abstract II
Acknowledgement III
Table of Contents IV
Table of Figures VI
Chapter 1: Introduction 1
1.1 Introduction to Shack Hartmann Wavefront Sensors 1
1.2 Motivation 2
1.3 Objective 3
Chapter 2: Shack Hartmann Wavefront Sensor 4
2.1 Historical Background 4
2.2 Measurement Principles of Shack-Hartmann Sensor 6
2.3 Selection of lenslet array and detector, 9
2.4 Dynamic range Vs. Sensitivity 10
Chapter 3: Literature Review 13
3.1 Software-based Approaches 13
3.1.1 Spiral: Local Similarity 15
3.1.2 Zernike Polynomial Extrapolation 17
3.1.3 Spline Extrapolation 18
3.2 Quality-guided Phase Unwrapping Algorithm 20
Chapter 4: Quality-Guided Algorithm 22
4.1 Priority Buffer 22
4.2 Location Prediction 25
4.2.1 Linear Prediction 26
4.2.2 Nonlinear Prediction 29
4.2.3 Slope Direction 31
Chapter 5: Results 36
5.1 Dynamic Range 37
5.2 Robustness to Missing Spots 45
5.3 Robustness to Local Defect 50
5.4 Computation Time 52
5.5 Experiments 53
5.6 Centroiding 60
5.7 Discussions 62
Chapter 6: Conclusion 64
Chapter 7: Future Work 65
References 66

參考文獻

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指導教授

梁肇文(Chao-Wen Liang)

審核日期

2013-8-16

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