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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 盧奕璋 | |
dc.contributor.author | Chun-Hsien Ho | en |
dc.contributor.author | 何俊憲 | zh_TW |
dc.date.accessioned | 2021-06-16T05:14:35Z | - |
dc.date.available | 2019-09-02 | |
dc.date.copyright | 2014-09-02 | |
dc.date.issued | 2014 | |
dc.date.submitted | 2014-08-18 | |
dc.identifier.citation | [1] M. Levoy and P. Hanrahan, 'Light field rendering,' in Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, 1996, pp. 31-42.
[2] B. Wilburn, N. Joshi, V. Vaish, E.-V. Talvala, E. Antunez, A. Barth, et al., 'High performance imaging using large camera arrays,' in ACM Transactions on Graphics (TOG), 2005, pp. 765-776. [3] C.-K. Liang, G. Liu, and H. H. Chen, 'Light Field Acquisition using Programmable Aperture Camera,' in Image Processing, 2007. ICIP 2007. IEEE International Conference on, 2007, pp. V - 233-V - 236. [4] C.-K. Liang, T.-H. Lin, B.-Y. Wong, C. Liu, and H. H. Chen, 'Programmable aperture photography: multiplexed light field acquisition,' in ACM Transactions on Graphics (TOG), 2008, p. 55. [5] R. Ng, M. Levoy, M. Brédif, G. Duval, M. Horowitz, and P. Hanrahan, 'Light field photography with a hand-held plenoptic camera,' Computer Science Technical Report CSTR, vol. 2, 2005. [6] C.-C. Chen, Y.-C. Lu, and M.-S. Su, 'Light field based digital refocusing using a DSLR camera with a pinhole array mask,' in Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on, 2010, pp. 754-757. [7] F. Moreno-Noguer, P. N. Belhumeur, and S. K. Nayar, 'Active refocusing of images and videos,' ACM Trans. Graph., vol. 26, p. 67, 2007. [8] D. Marr and T. Poggio, 'A computational theory of human stereo vision,' Proceedings of the Royal Society of London. Series B. Biological Sciences, vol. 204, pp. 301-328, 1979. [9] D. Scharstein and R. Szeliski, 'A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,' International journal of computer vision, vol. 47, pp. 7-42, 2002. [10] B. D. Lucas and T. Kanade, 'An iterative image registration technique with an application to stereo vision,' in Proceedings of the 7th international joint conference on Artificial intelligence, 1981. [11] M. Bleyer and M. Gelautz, 'A layered stereo algorithm using image segmentation and global visibility constraints,' in Image Processing, 2004. ICIP '04. 2004 International Conference on, 2004, pp. 2997-3000 Vol. 5. [12] A. Zomet and S. K. Nayar, 'Lensless Imaging with a Controllable Aperture,' in Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, 2006, pp. 339-346. [13] Z. Cha and C. Tsuhan, 'Light field capturing with lensless cameras,' in Image Processing, 2005. ICIP 2005. IEEE International Conference on, 2005, pp. III-792-5. [14] D. Reddy, J. Bai, and R. Ramamoorthi, 'External Mask Based Depth and Light Field Camera,' in Computer Vision Workshops (ICCVW), 2013 IEEE International Conference on, 2013, pp. 37-44. [15] Z. Changyin and S. K. Nayar, 'Computational Cameras: Convergence of Optics and Processing,' Image Processing, IEEE Transactions on, vol. 20, pp. 3322-3340, 2011. [16] D. Scharstein and R. Szeliski, 'A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,' International journal of computer vision, vol. 47, pp. 7-42, 2002. [17] H. Hirschmuller, 'Accurate and efficient stereo processing by semi-global matching and mutual information,' in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, 2005, pp. 807-814 vol. 2. [18] H. Hirschmüller, M. Buder, and I. Ernst, 'Memory efficient semi-global matching,' ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 3, pp. 371-376, 2012. [19] M. Michael, J. Salmen, J. Stallkamp, and M. Schlipsing, 'Real-time stereo vision: Optimizing Semi-Global Matching,' in Intelligent Vehicles Symposium (IV), 2013 IEEE, 2013, pp. 1197-1202. [20] Y.-C. Zeng 'Generation of all-focus images and depth-adjustable images based on pixel blurriness,' in Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2013 Asia-Pacific, 2013, pp. 1-9. [21] L. Rayleigh, 'On pin-hole photography,' The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, vol. 31, pp. 87-99, 1891. [22] C.-W. Chang, 'Design of Pinhole Array Masks and Image Processing Algorithms for Light Field Cameras,' M.S. Thesis, National Taiwan University, 2012. [23] M. Brown and D. G. Lowe, 'Automatic panoramic image stitching using invariant features,' International journal of computer vision, vol. 74, pp. 59-73, 2007. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/56070 | - |
dc.description.abstract | 現有的手持式光場相機是在透鏡與感光器中間加入微透鏡陣列或是針孔陣列遮罩,記錄四維光場資訊,和一般相機相比,可以進行數位變焦及生成場景深度圖等應用。
在本論文中,我們將相機的鏡頭拆除,將針孔陣列遮罩放置於相機之感光器前,形成一無透鏡光場相機系統,因為少了鏡頭,整個相機的成本、重量與體積都減少了許多,可增加攜帶的便利性。光線經由個別針孔所投影出的子影像,皆可視為一獨立的針孔相機成像。因此,我們只需一次快門便可得到一組針孔相機陣列的光場資料。因為每個子影像視角皆不相同,我們可以綜合運用子影像的資訊進行深度估測,並將個別子影像拼接成一較大影像,然後進行數位變焦,模擬透鏡的對焦效果。 基於深度估測在演算法流程中耗時最長,且以硬體實踐數位變焦演算法的效益不高,在論文的最後,我們以積體電路實作無透鏡光場相機資料深度估測處理器。對於3648×2280之光場資料,可以於2.1秒內完成子影像深度估測,與軟體版本相比加速可達3倍。以TSMC90奈米製程實現,晶片尺寸為1.49×1.49 mm2,核心尺寸為1.029×1.029 mm2,在操作頻率125 MHz下之消耗功率為111.4 mW。 | zh_TW |
dc.description.abstract | Hand-held light field cameras can provide additional applications, such as depth estimation and digital refocusing, which are not available through conventional digital cameras.
In this thesis, we design a lensless light field camera system by removing the camera lens and placing a pinhole array mask in front of the sensor. Without a camera lens, the cost, weight and volume of cameras can be reduced. Each sub-image can be regarded as a single image captured by a small pinhole camera. Therefore, we are able to obtain a set of pinhole camera array data with only one single shot. Due to different view angles between sub-images, we can apply depth estimation technique to sub-image pairs and combine all sub-images into a larger image. Digital refocusing can be achieved using the data from these sub-images as well. In the last part of the thesis, we implement a depth estimation processor for lensless light field data using TSMC 90 nm technology. This processor is able to process a 3648×2280 light field image and generate a 500×400 depth map within 2.1 s. The chip size is 2.2201 mm2. The power consumption is 111.4 mW when operating at 125 MHz. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T05:14:35Z (GMT). No. of bitstreams: 1 ntu-103-R01943017-1.pdf: 3647295 bytes, checksum: 17173044a432ebbe167342da3492d1ff (MD5) Previous issue date: 2014 | en |
dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii ABSTRACT iv 目錄 v 圖目錄 viii 表目錄 xi 第1章 緒論 1 1.1 光場簡介 1 1.2 取得光場資料方法 2 1.3 數位變焦 3 1.4 場景深度估測 4 1.5 無透鏡相機 5 1.6 論文架構 6 第2章 一般使用透鏡光場相機 7 2.1 外部遮罩光場相機 7 2.1.1 外部遮罩光場相機設計 7 2.1.2 光場特性 8 2.1.3 拍攝之光場資料 12 2.2 深度估測演算法 13 2.2.1 視差之成因 13 2.2.2 半全域性比對 15 2.3 數位變焦演算法 18 2.4 生成全對焦影像 21 2.5 本章結論 23 第3章 無透鏡光場相機 24 3.1 無透鏡光場相機介紹 24 3.1.1 無透鏡光場相機設計 24 3.1.2 光場分析 27 3.2 拍攝之光場資料 30 3.2.1 原始光場資料 30 3.2.2 影像前處理 31 3.3 深度估測 32 3.3.1 無透鏡光場相機深度估測能力 32 3.3.2 深度估測結果 35 3.4 影像拼接 36 3.5 數位變焦 39 3.6 本章結論 41 第4章 無透鏡光場相機之深度估測硬體架構設計 43 4.1 硬體架構 43 4.1.1 位址控制器 44 4.1.2 資料項計算器 45 4.1.3 平滑項計算器 46 4.1.4 成本累加器與深度判斷器 47 4.2 管線化架構 48 4.3 硬體結果 49 第5章 結論與展望 53 5.1 結論 53 5.2 展望 53 參考文獻 54 | |
dc.language.iso | zh-TW | |
dc.title | 用於無透鏡光場相機資料之數位變焦與深度估測演算法 | zh_TW |
dc.title | Depth Estimation and Digital Refocusing Algorithms for Lensless Light Field Camera Data | en |
dc.type | Thesis | |
dc.date.schoolyear | 102-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 丁建均,王傑智,林晃嚴 | |
dc.subject.keyword | 光場,針孔相機,深度估測,數位變焦,硬體設計, | zh_TW |
dc.subject.keyword | light field,pinhole photography,depth estimation,digital refocusing,hardware implementation, | en |
dc.relation.page | 56 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2014-08-18 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電子工程學研究所 | zh_TW |
顯示於系所單位: | 電子工程學研究所 |
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