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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 莊永裕 | |
dc.contributor.author | Pei-Ying Lu | en |
dc.contributor.author | 呂培瑛 | zh_TW |
dc.date.accessioned | 2021-06-14T17:16:52Z | - |
dc.date.available | 2008-08-05 | |
dc.date.copyright | 2008-08-05 | |
dc.date.issued | 2008 | |
dc.date.submitted | 2008-07-25 | |
dc.identifier.citation | [1] M. Ashikhmin. A tone mapping algorithm for high contrast images. In EGRW ’02: Proceedings of the 13th Eurographics workshop on Rendering, pages 145–156,
Aire-la-Ville, Switzerland, Switzerland, 2002. Eurographics Association. [2] L. Bar, N. Sochen, and N. Kiryati. Semi-blind image restoration via mumford-shah regularization. IEEE Trans. on Image Processing, 15(2):483–493, 2006. [3] B. Bascle, A. Blake, and A. Zisserman. Motion deblurring and super-resolution from an image sequence. In ECCV, pages 573–582, 1996. [4] P. E. Debevec and J. Malik. Recovering high dynamic range radiance maps from photographs. In SIGGRAPH ’97: Proceedings of the 24th annual conference on Computer graphics and interactive techniques, pages 369–378, New York, NY, USA, 1997. ACM Press/Addison-Wesley Publishing Co. [5] R. Fattal, D. Lischinski, and M. Werman. Gradient domain high dynamic range compression. ACM Trans. Graph., 21(3):249–256, 2002. [6] R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman. Removing camera shake from a single photograph. ACM Trans. Graph., 25(3):787–794, 2006. [7] D. Geman and G. Reynolds. Constrained restoration and the recovery of discontinuities. IEEE Trans. Pattern Anal. Mach. Intell., 14(3):367–383, 1992. [8] P. C. Hansen, J. G. Nagy, and D. P. O’Leary. Deblurring Images: Matrices, Spectra, and Filtering (Fundamentals of Algorithms). Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 2006. [9] M. H. HeinzW. Engl and A. Neubauer. Regularization of inverse problems. Kluwer Academic, Boston, Dordrecht, 2000. [10] J. Jia. Single image motion deblurring using transparency. In CVPR, 2007. [11] R. Neelamani, H. Choi, and R. Baraniuk. Forward: Fourier-wavelet regularized deconvolution for ill-conditioned systems, 2004. [12] A. Rav-Acha and S. Peleg. Two motion-blurred images are better than one. Pattern Recogn. Lett., 26(3):311–317, 2005. [13] E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda. Photographic tone reproduction for digital images. ACM Trans. Graph., 21(3):267–276, 2002. [14] W. H. Richardson. Bayesian-based iterative method of image restoration. Journal of the Optical Society of America (1917-1983), 62:55–59, 1972. [15] M. A. Robertson, S. Borman, and R. L. Stevenson. Estimation-theoretic approach to dynamic range enhancement using multiple exposures. Journal of Electronic Imaging, 12:219–228, Apr. 2003. [16] G. Ward. Fast, robust image registration for compositing high dynamic range photographs from handheld exposures, 2003. [17] L. Yuan, J. Sun, L. Quan, and H.-Y. Shum. Image deblurring with blurred/noisy image pairs. In SIGGRAPH ’07: ACM SIGGRAPH 2007 papers, page 1, New York, NY, USA, 2007. ACM. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/41099 | - |
dc.description.abstract | 本論文提出了一個能克服因手振而導致影像模糊的限制,利用手持相機拍攝多張不同曝光時間的照片來合成清晰之高動態範圍影像。
利用多張不同曝光時間的照片來合成清晰之高動態範圍影像是一個十分常見的方法, 然而這些方法卻限制在拍攝這些多張不同曝光時間的照片必須靜止不動,亦即在拍攝時需要腳架或特殊硬體的輔助。 為了能突破這些限制,我們必須克服手持相機所產生的兩個主要問題:第一,照片間的對齊問題,第二,拍攝的照片容易因手振導致模糊,尤其在曝光時間愈長時,情況愈嚴重。 為克服這些問題,我們針對不同曝光時間的照片先做對齊後,並提出一個新的演算法運用這多張可能有模糊的影像來合成清晰之高動態範圍影像。 在我們提出的演算法中,我們假設這多張不同曝光時間的照片都是由同一張清晰的高動態範圍影像但經過不同的 晃動路徑及曝光時間而成。利用這多張影像的資訊,運用貝氏定理定義出問題的機率模型,透過反覆地解出每張影像對應的晃動路徑, 並反覆地更新合成出來的高動態範圍影像,最後更新相機的函數,依此不斷地重複更新此三項參數直到演算法收斂為止。 最後我們將可求得令人滿意且擁有豐富及清楚細節的高動態範圍影像。 為證實我們演算法的有效性,我們將對合成的影像以及實際拍攝的影像進行測試,並跟其他兩個相關的方法做比較。 | zh_TW |
dc.description.abstract | In this thesis, we present a novel technique to reconstruct a high quality high dynamic range image
from a set of differently exposed and possibly blurred images from a hand-held camera. Compositing high dynamic range image from differently exposed photographs is a common approach, however, it often requires a tripod to keep the camera still when taking photographs of different exposures. To ease the process, one often prefer to use hand-held cameras. This, however, leads to two problems, misaligned photographs and blurred long-exposed photographs. To overcome these problems, we adapt an alignment method and propose a method for HDR reconstruction from possibly blurred images. We conqure the blurry artifact caused by camera shake from hand-held cameras to reconstruct a clear high dynamic range image. Instead of applying the naive solution that separately deblur each photograph and then composite these deblurred results to construct a high dynamic range image, we seek to utilize the correlation among blurred images, based on the assumption that all observations come from the same latent high dynamic range image convolved by different blur kernels. We adopt a Bayesian approach to define our probability model and apply maximum-likelihood approach to iteratively perform kernel estimation, high dynamic range image reconstruction and camera curve recovery. Until convergence, we obtain an satisfying high dynamic range image with rich and clear structure. To demonstrate the effectiveness of our algorithm, we tested our implementation on both synthetic examples and real photographs and compared favorably to two related methods. | en |
dc.description.provenance | Made available in DSpace on 2021-06-14T17:16:52Z (GMT). No. of bitstreams: 1 ntu-97-R95922013-1.pdf: 2602038 bytes, checksum: 8d393a5b6c85e2bbb67c5e7df5e6a840 (MD5) Previous issue date: 2008 | en |
dc.description.tableofcontents | 口試委員會審定書i
致謝iii 中文摘要v Abstract vii 1 Introduction 1 1.1 Motivation . . . . . . . .. . . . . . . . . . . . 1 1.2 Problem Statement . . . . . . . . . . . . . . . . 2 1.3 Thesis Organization . . . . . . . . . .. . . . . . . . . 2 2 Related Work 3 2.1 Camera Pipeline and HDR Fusion. . . . . . . . . . . 3 2.1.1 HDR Reconstruction . . . . . . . . . . . . . . 4 2.1.2 HDR Tonemapping . . . . . . .. . . . . . . . . 5 2.2 Image Deblurring . . . . . .. . . . . . . . . . . 6 2.2.1 Blind Deconvolution . . . . . . . . . . . . . 6 2.2.2 Non-blind Deconvolution . . . . . . . . . . . . . 7 3 Overview 9 4 Image Alignment 11 4.1 Binarization . . . . .. . . . . . . . .. . 12 4.2 Multi-scale Image Alignment . . . . . . . . . . . 12 5 Algorithm 19 5.1 Our Model . . . . . . . . . . . . . . . . . . . 19 5.2 Optimization . . . . . . . . . . . . . . . . . . 21 5.2.1 Optimizing Ki . . . . . . . . . . .. . . . . . . 21 5.2.2 Optimizing E . . . . . . . . . .. . . . . . 22 5.2.3 Optimizing g . . . . . . . . . 23 5.3 Optimization Details and Parameters . . . . . . . . . . 25 5.3.1 Tikhonov Regularization Method . . . . . . . . . . 25 5.3.2 Irradiance Scaling . . . . . . .. . . . . . 26 5.3.3 Color Handling . . . . . . . . . . . . . . . 26 6 Experiments and Results 29 6.1 Synthetic Examples . . . . . . . . . . . . . . . . . 29 6.2 Real Photographs . . . . . . . . . . . . . . . . . 31 7 Conclusion and Discssion 47 Bibliography 49 | |
dc.language.iso | en | |
dc.title | 克服手振因素之清晰高動態範圍影像合成 | zh_TW |
dc.title | High Dynamic Range Image Reconstruction from Hand-held
Cameras | en |
dc.type | Thesis | |
dc.date.schoolyear | 96-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 歐陽明,梁容輝 | |
dc.subject.keyword | 高動態範圍影像,移除模糊,模糊核心,相機響應函數, | zh_TW |
dc.subject.keyword | High dynamic range images,Deblurring,Blur kernel,Camera response curve, | en |
dc.relation.page | 50 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2008-07-27 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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