結合散射介質的單像素光譜成像系統

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姓名

李旻鴻(Min-Hung Lee) 查詢紙本館藏

畢業系所

光電科學與工程學系

論文名稱

結合散射介質的單像素光譜成像系統
(An Imaging Spectrometer Using a Scattering Medium and Single-Pixel Imaging)

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至系統瀏覽論文 (2026-1-20以後開放)

摘要(中)

紅外光光譜影像系統可提供物質中分子組成的二維空間分布資訊，因此這樣的系統已被應用於許多領域中，如食物品質分析。為了拍攝物體於紅外光波段的影像，常見的做法是以二維陣列偵測器或單點掃描的干涉儀架構取得物體影像，但二維陣列偵測器在紅外光波段的價格通常昂貴，且干涉儀架構通常對系統穩定度的要求較高，因此紅外光光譜影像系統較難以普及於日常生活中的應用。
為了突破上述限制，本研究架設一結合散射介質與單像素成像技術的光譜影像系統，以單像素成像還原帶有物體空間與光譜資訊的光斑影像後，再依靠反摺積重建物體光譜影像。為了測試系統架構的可行性，目前以可見光波段進行實驗。當點光源通過散射介質後，於成像面上產生的光斑圖案具有記憶效應與光譜去相關性，因此在取得物體於成像面形成的光斑影像後，便可利用反摺積還原物體光譜影像。本研究成功以此作法還原單色物體影像與三色物體的光譜影像。接著模擬系統以單像素成像還原物體光斑影像的過程，並以反摺積成功重建單色物體影像。但實際以系統進行單像素成像與反摺積的影像重建流程後，發現系統無法重建單色物體影像。在先前模擬的流程中加入雜訊後，我們確認影像還原過程中的雜訊影響物體影像重建。未來若能提高入射系統的光強度，或提升偵測器的靈敏度，便有機會提高訊雜比，減少雜訊對物體影像重建的影響。

摘要(英)

An infrared (IR) spectral imaging system is able to provide information on the two-dimensional spatial distribution of the molecular composition of an object, and there has been many applications of such a system, for example food quality analysis. An image of the object in the IR region is commonly taken by a two-dimensional focal plane array (FPA) or a single-point scanning interferometer, but an IR FPA is often expensive, and an interferometer often requires stable environmental conditions; thus, it is harder for an IR spectral imaging system to be commonly applied to applications in daily life.
To overcome these limitations, a system combining a scattering medium and single-pixel imaging technique is set up in our research. After recovering the speckle pattern containing the spatial and spectral information of an object, the spectral image of the object can be reconstructed by deconvolution. To test the feasibility of the system setup, the experiment is carried out in the visible region. After a point light source passes through a scattering medium, the speckle pattern formed in the image plane has the property of memory effect and spectral decorrelation; thus, the spectral image of an object can be recovered by deconvolution after obtaining the speckle image formed by an object in the image plane. The image of a monochromatic object and the spectral image of an object with red, green, and blue colors are successfully recovered by the proposed method in our research. By simulating the process of recovering the speckle image of an object by a system using single-pixel imaging, an image of the monochromatic object is reconstructed by the deconvolution. However, after performing the process of image reconstruction using single-pixel imaging and deconvolution by a real system, it was discovered that the system is unable to recover the image of a monochromatic object. After adding noises into the previous simulation process, we confirmed that the reconstruction of an object’s image is affected by noises in the process of recovering an image. If the intensity of light illuminating the system is higher, or the sensitivity of the detector is improved, it might be possible to improve the signal-to-noise ratio; thus, the effect of noises on the reconstruction of an object’s image can be reduced.

關鍵字(中)

★ 單像素成像
★ 光譜影像系統

關鍵字(英)

論文目次

摘要 i
Abstract ii
致謝 iv
目錄 v
第一章緒論 1
1.1 研究動機與目的 1
1.2 文獻回顧與探討 1
1.2.1 運用紅外光光譜影像系統檢測食品實例 2
1.2.2 傳統光譜影像系統 3
1.2.3 Fourier轉換紅外光譜儀 4
1.2.4 單像素光譜影像系統 5
1.2.5 結合散射介質的單像素成像系統 9
1.3 論文架構 11
第二章單像素光譜影像重建法 13
2.1 散射介質與光譜影像重建 13
2.2 單像素成像 16
2.2.1 取像流程 16
2.2.2 影像重建法 16
2.2.3 調制圖像 18
2.3 結合散射介質的單像素光譜影像系統 20
第三章系統架構 22
3.1 系統架構 22
3.2 光學元件選擇 24
3.2.1 散射介質 24
3.2.2 空間光調制元件 25
3.3 儀器控制 27
第四章實驗結果 30
4.1 利用相機和散射介質重建光譜影像 30
4.1.1 以反摺積重建物體光譜影像 30
4.1.2 系統解析度量測 34
4.1.3 散射介質記憶效應量測 36
4.2 以單像素成像還原物體光譜影像 37
4.2.1 單色光物體影像重建模擬 38
4.2.2 以單像素光譜影像系統重建單色光物體影像 42
4.2.3 模擬雜訊對於光斑影像重建的影響 47
第五章結論 51
參考文獻 53

參考文獻

1. R. Salzer and H. W. Siesler, Infrared and Raman Spectroscopic Imaging, 2nd edition (Wiley, 2014).
2. L. M. Kandpal, S. Lee, M. S. Kim, H. Bae, and B.-K. Cho, “Short wave infrared (SWIR) hyperspectral imaging technique for examination of aflatoxin B1 (AFB1) on corn kernels,” Food Control 51, 171-176 (2015).
3. X. Chua, W. Wang, S.-C. Yoon, X. Ni, and G. W. Heitschmidt, “Detection of aflatoxin B1 (AFB1) in individual maize kernels using short wave infrared (SWIR) hyperspectral imaging,” Biosyst. Eng. 157, 13-23 (2017).
4. D. Kimuli, W. Wang, W. Wang, H. Jiang, X. Zhao, and X. Chu, “Application of SWIR hyperspectral imaging and chemometrics for identification of aflatoxin B1 contaminated maize kernels,” Infrared Phys. Technol. 89, 351-362 (2018).
5. W.-H. Su and D.-W. Sun, “Multispectral imaging for plant food quality analysis and visualization,” Compr. Rev. Food Sci. Food Saf. 17, 220-239 (2018).
6. R. Siche, R. Vejarano, V. Aredo, L. Velasquez, E. Saldaña, and R. Quevedo, “Evaluation of food quality and safety with hyperspectral imaging (HSI),” Food Eng. Rev. 8, 306-322 (2016).
7. G. Gauglitz and D. S. Moore, Handbook of Spectroscopy, 2nd edition (Wiley, 2014).
8. Y. Garini, I. T. Young, and G. McNamara, “Spectral imaging: principles and applications,” Cytometry A 69A(8), 735–747 (2006).
9. Q. Li, X. He, Y. Wang, H. Liu, D. Xu, and F. Guo, “Review of spectral imaging technology in biomedical engineering: achievements and challenges,” J. Biomed. Opt. 18(10), 100901 (2013).
10. P. R. Griffiths and J. A. de Haseth, Fourier Transform Infrared Spectrometry, 2nd edition (Wiley, 2006).
11. M. Eismann, Hyperspectral Remote Sensing (SPIE Press, 2012).
12. N. P. Ayerden, U. Aygun, S. T. S. Holmstrom, S. Olcer, B. Can, J.-L. Stehle, and H. Urey, “High-speed broadband FTIR system using MEMS,” Appl. Opt. 53, 7267-7272 (2014).
13. H. R. Seren, S. Holmstrom, N. P. Ayerden, J. Sharma, and H. Urey, “Lamellar-grating-based MEMS Fourier transform spectrometer,” J. Microelectromech. Syst. 21, 331–339 (2012).
14. M. Duarte, M. Davenport, D. Takhar, J. Laska, T. Sun, K. Kelly, and R. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Processing Magazine 25, 83–91 (2008).
15. M. P. Edgar, G. M. Gibson, and M. J. Padgett, “Principles and prospects for single-pixel imaging,” Nat. Photonics 13, 13–20 (2019).
16. G. M. Gibson, S. D. Johnson, and M. J. Padgett, “Single-pixel imaging 12 years on: a review,” Opt. Express 28, 28190-28208 (2020).
17. R. I. Stantchev, X. Yu, T. Blu, and E. J. N. C. Pickwell-MacPherson, “Real-time terahertz imaging with a single-pixel detector,” Nat. Commun. 11(1), 2535 (2020).
18. Y. Jauregui-Sánchez, P. Clemente, J. Lancis, and E. Tajahuerce, “Single-pixel imaging with Fourier filtering: application to vision through scattering media,” Opt. Lett. 44, 679-682 (2019).
19. Z.-H. Xu, W. Chen, J. Penuelas, M. Padgett, and M.-J. Sun, “1000 fps computational ghost imaging using LED-based structured illumination,” Opt. Express 26(3), 2427–2434 (2018).
20. G. M. Gibson, B. Sun, M. P. Edgar, D. B. Phillips, N. Hempler, G. T. Maker, G. P. A. Malcolm, and M. J. Padgett, “Real-time imaging of methane gas leaks using a single-pixel camera,” Opt. Express 25, 2998-3005 (2017).
21. M. J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016).
22. L. Bian, J. Suo, G. Situ, Z. Li, J. Fan, F. Chen, and Q. Dai, “Multispectral imaging using a single bucket detector,” Sci. Rep. 6, 24752 (2016).
23. Y. Wang, J. Suo, J. Fan, and Q. Dai, “Hyperspectral Computational Ghost Imaging via Temporal Multiplexing,” IEEE Photonics Technol. Lett. 28, 288-291 (2016).
24. Z. Zhang, X. Ma, and J. Zhong, “Single-pixel imaging by means of Fourier spectrum acquisition,” Nat. Commun. 6(1), 6225 (2015).
25. B. Sun, M. P. Edgar, R. Bowman, L. E. Vittert, S. Welsh, A. Bowman, and M. J. Padgett, “3D computational imaging with single-pixel detectors,” Science 340, 844–847 (2013).
26. X. Liu, J. Shi, L. Sun, Y. Li, J. Fan, and G. Zeng, “Photon-limited single-pixel imaging,” Opt. Express 28, 8132-8144 (2020).
27. P. Gattinger, J. Kilgus, I. Zorin, G. Langer, R. Nikzad-Langerodi, C. Rankl, M. Gröschl, and M. Brandstetter, “Broadband near-infrared hyperspectral single pixel imaging for chemical characterization,” Opt. Express 27, 12666-12672 (2019).
28. D. B. Phillips, M.-J. Sun, J. M. Taylor, M. P. Edgar, S. M. Barnett, G. M. Gibson, and M. J. Padgett, “Adaptive foveated single-pixel imaging with dynamic supersampling,” Sci. Adv. 3(4), e1601782 (2017).
29. J. Senlin, H. Wangwei, W. Yunlong, H. Kaicheng, S. Qiushuai, Y. Cuifeng, L. Dongqi, Ye Qing, Z. Wenyuan, and T. Jianguo, “Hyperspectral imaging using the single-pixel Fourier transform technique,” Sci. Rep. 7, 45209 (2017).
30. Z. Li, J. Suo, X. Hu, C. Deng, J. Fan, and Q. Dai, “Efficient single-pixel multispectral imaging via non-mechanical spatio-spectral modulation,” Sci. Rep. 7, 41435 (2017).
31. M.-J. Sun, M. P. Edgar, D. B. Phillips, G. M. Gibson, and M. J. Padgett, “Improving the signal-to-noise ratio of single-pixel imaging using digital microscanning,” Opt. Express 24, 10476-10485 (2016).
32. S. K. Sahoo, D. Tang, and C. Dang, “Single-shot multispectral imaging with a monochromatic camera,” Optica 4, 1209-1213 (2017).
33. J. W. Goodman, Speckle Phenomena in Optics: Theory and Applications, 2nd edition (SPIE Press, 2020).
34. S. Feng, C. Kane, P. A. Lee, and A. D. Stone, “Correlations and fluctuations of coherent wave transmission through disordered media,” Phys. Rev. Lett. 61, 834–837 (1988).
35. I. Freund, M. Rosenbluh, and S. Feng, “Memory effects in propagation of optical waves through disordered media,” Phys. Rev. Lett. 61, 2328–2331 (1988).
36. J. Bertolotti, E. G. van Putten, C. Blum, A. Lagendijk, W. L. Vos, and A. P. Mosk, “Non-invasive imaging through opaque scattering layers,” Nature 491, 232–234 (2012).
37. O. Katz, P. Heidmann, M. Fink, and S. Gigan, “Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations,” Nat. Photonics 8, 784–790 (2014).
38. E. Edrei and G. Scarcelli, “Memory-effect based deconvolution microscopy for super-resolution imaging through scattering media,” Sci. Rep. 6, 33558 (2016).
39. J. W. Goodman, Introduction to Fourier Optics, 3rd edition (Roberts & Company, 2005).
40. F. Rousset, “Single-pixel imaging: Development and applications of adaptive methods,” Ph.D. Thesis, Université de Lyon (2017).
41. C. Li, W. Yin, H. Jiang, and Y. Zhang, “An efficient augmented lagrangian method with applications to total variation minimization,” Comput. Optim. Appl. 56(3), 507–530 (2013).
42. K. M. Czajkowski, A. Pastuszczak, and R. Kotyński, “Real-time single-pixel video imaging with Fourier domain regularization,” Opt. Express 26, 20009-20022 (2018).
43. X. Yu, F. Yang, B. Gao, J. Ran and X. Huang, “Deep compressive single pixel imaging by reordering Hadamard basis: A comparative study,” IEEE Access 8, 55773-55784 (2020).
44. Z. Zhang, X. Wang, G. Zheng, and J. Zhong, “Hadamard single-pixel imaging versus Fourier single-pixel imaging,” Opt. Express 25, 19619-19639 (2017).
45. K. J. Horadam, Hadamard Matrices and Their Applications (Princeton University Press, 2007).
46. W.-K. Yu, “Super sub-Nyquist single-pixel imaging by means of cake-cutting Hadamard basis sort,” Sensors 19(19), 4122 (2019).
47. P. G. Vaz, D. Amaral, L. F. R. Ferreira, M. Morgado, and J. Cardoso, “Image quality of compressive single-pixel imaging using different Hadamard orderings,” Opt. Express 28, 11666-11681 (2020).
48. F. Soldevila, P. Clemente, E. Tajahuerce, N. Uribe-Patarroyo, A. P., and J. Lancis, “Computational imaging with a balanced detector,” Sci. Rep. 6, 29181 (2016).
49. M. P. Edgar, S. Johnson, D. Phillips, and M. J. Padgett, “Real-time computational photon-counting lidar,” Opt. Eng. 57, 031304 (2018).
50. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th edition (Pearson, 2018).
51. https://www.edmundoptics.com/knowledge-center/application-notes/optics/diffuser-selection-guide/.
52. J. Chen, W. Cranton, and M. Fihn, Handbook of Visual Display Technology, 2nd edition (Springer, 2016).
53. S. Scholes, R. Kara, J. Pinnell, V. Rodríguez-Fajardo, and A. Forbes, “Structured light with digital micromirror devices: a guide to best practice,” Opt. Eng. 59, 1–12 (2019).
54. Z. Zhang, Z. You, and D. Chu, “Fundamentals of phase-only liquid crystal on silicon (LCOS) devices,” Light Sci. Appl. 3, e135 (2014).
55. N. Matsumoto, T. Ando, T. Inoue, Y. Ohtake, N. Fukuchi, and T. Hara, “Generation of high-quality higher-order Laguerre-Gaussian beams using liquid-crystal-on-silicon spatial light modulators,” J. Opt. Soc. Am. A 25, 1642-1651 (2008).
56. M.-J. Sun and J.-M. Zhang, “Single-pixel imaging and its application in three-dimensional reconstruction: a brief review,” Sensors 19(3), 732 (2019).
57. https://www.jasperdisplay.com/slm-products/srk-overview/
58. https://e2e.ti.com/support/dlp/f/94/t/323184
59. H. Zhuang, H. He, X. Xie, and J. Zhou, “High speed color imaging through scattering media with a large field of view,” Sci. Rep. 6, 32696 (2016).
60. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600-612 (2004).

指導教授

陳思妤(Szu-Yu Chen)

審核日期

2021-1-20

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