請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/38002完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 丁建均 | |
| dc.contributor.author | Yu-Si Zhang | en |
| dc.contributor.author | 張育思 | zh_TW |
| dc.date.accessioned | 2021-06-13T15:55:53Z | - |
| dc.date.available | 2011-06-24 | |
| dc.date.copyright | 2008-06-24 | |
| dc.date.issued | 2008 | |
| dc.date.submitted | 2008-06-13 | |
| dc.identifier.citation | 118
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[Contourlets] 120 [21] M. N. Do and M. Vetterli, “The contourlet transform: an efficient directional multiresolution image representation,” IEEE Trans. Image Process, vol. 14, no. 12, Dec 2005. [22] M. N. Do, Directional Multiresolution Image Representations, Ph.D. dissertation, School Comput. Commun. Sci., Swiss Fed. Inst. Technol., Lausanne, Switzerland, 2001, available from http://biblion.epfl.ch/EPFL/theses/2001/2500/EPFL_TH2500.pdf [23] R. Ansari, “Efficient IIR and FIR fan filters,” IEEE Trans. Circuits Syst, vol. 34, pp. 941-945, Aug. 1987. [24] R. H. Bamberger and J. T. Smith, “A filter bank for the directional decomposition of images: theory and design,” IEEE Trans. Signal Process, vol. 40, pp. 882-893, 1992. [Shearlets] [25] G. Kutyniok and T. Sauer, Adaptive Directional Subdivision Schemes and Shearlet Multiresolution Analysis,” 2007, available in http://www.shearlet.org/. [26] S. Dahlke, G. Kutyniok, G. Steidl, and G. Teschke, “Shearlet coorbit spaces and associated banach frames,” 2007, available in http://www.shearlet.org/. [27] G. Easley, W. Lim, and D. Labate, “Sparse directional image representations using the discrete shearlet transform,” 2006, available in http://www.shearlet.org/. [28] G. Kutyniok and D. Labate, “Resolution of the wavefront set using continuous shearlets,” 2006, available in http://www.shearlet.org/. [29] G. Kutyniok and T. Sauer, “From wavelets to shearlets and back again,” 2007, available in http://www.ahearlet.org/. [30] K. Guo and D. Labate, “Representation of Fourier integral operators using shearlets,” available in http://www.shearlet.org/. 121 [31] S. Dahlke, G. Kutyniok, P. Maass, C. Sagiv, H.-G. Stark, and G. Teschke, “The uncertainty principle associated with the continuous shearlet transform,” available in http://www.shearlet/org/. [32] K. Guo and D. Labate, “Optimally sparse multidimensional representation using shearlets,” SIAM J. 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Ma, and X. han, “Multiscale feature extraction of finger-vein patterns based on curvelets and local interconnection structure neural network,” ICPR'06, 2006. [46] F.M. Kazemu, S. Samadi, H.R. Poorreza, and M. Akbarzadeh-T, “Vehicle recognition based on Fourier, wavelet and curvelet transforms- a comparative study,” ITNG'07, 2007. [47] J. Zhang, Z. Zhang, W. Huang, Y. Lu, and Y. Wang, “Face recognition based on curvefaces,” ICNC 2007, 2007. [48] J. Starck, F. Murtagh, E. J. Candes, and D. L. Donoho, “Gray and color image contrast enhancement by the curvelet transform,” IEEE Trans. on Image Proc., vol. 12, no. 6, June 2003. [49] A. Garzelli, F. Nencini, L. Alparone, and S. Baronti, “Multiresolution fusion of multispectral and panchromatic images through the curvelet transform,” IEEE, 2005. [50] Jayalakshmi M., S. N. Merchant, U. B. Desai, “Digital watermarking in contourlet domain,” ICPR'06, 2006. 123 [51] N. Baaziz, “Adaptive watermarking schemes based on a redundant contourlet transform,” IEEE, 2005. [52] H. Li, W. Song, and S. Wang, “A novel blind watermarking algorithm in contourlet domain,” ICPR'06, 2006. [53] Z. Xu, K. Wang, and X. H. Qiao, “A novel watermarking scheme in contourlet domain based on independent component analysis,” IIH-MSP'06, 2006. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/38002 | - |
| dc.description.abstract | 小波轉換最重要的特性即為可使用少量的小波轉換係數近似一個信號。因為
這個特性，JPEG 2000 納入了小波轉換，做為其演算法的一部分。 然而，此一特性的理論基礎皆是根據一維信號而來。雖然我們可以使用可分 離小波轉換將一維小波轉換擴展至二維小波轉換。但可分離小波轉換乎略了二維 信號相較於一維信號，有較豐富的幾何特性，如邊緣。 既然二維信號有更多的特性，許多學者便開使研究專門針對二維信號的轉 換，使其不但擁用小波轉換所擁有的特性，且提供優於可分離小波轉換的效果。 本論文著眼於分析這些方法的精神、利弊，並加以改善，使其更加符合實用 所需的條件。 | zh_TW |
| dc.description.abstract | The most important feature of the wavelet transform is that we can use few wavelet
coefficients to approximate a signal. Because of this property, JPEG 2000 adopted the wavelet transform as a portion of its algorithm. However, the fundamental theory of this feature was derived from one-dimensional signals. For two-dimensional signals, we can use “separable wavelet transform” to extend one-dimensional wavelet transform into two-dimensional wavelet transform. Although this method was used widely, it ignored the geometric properties of the two-dimensional signal such as edges. Since two-dimensional signals have more features, many researchers started to propose a new transform such that the new transform not only has all features of the wavelet transform but also exploit the properties of the two-dimensional signals. Furthermore, the performance is better than that of the separable wavelet transform. This thesis focuses on the ideas, advantages, and disadvantages of these new transforms. After discussing these methods, we propose our method to improve the performance. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T15:55:53Z (GMT). No. of bitstreams: 1 ntu-97-R95942093-1.pdf: 4007543 bytes, checksum: d757b59516f9fb03b2de23cb18c8ccf4 (MD5) Previous issue date: 2008 | en |
| dc.description.tableofcontents | CONTENTS
口試委員會審定書...........................................................................................................# 誌謝...................................................................................................................................i 中文摘要......................................................................................................................... iii ABSTRACT ......................................................................................................................v Chapter 1 Introduction.....................................................................................................1 1.1 Motivation.......................................................................................................1 1.2 Related Work ..................................................................................................2 1.3 Thesis Outline.................................................................................................3 Chapter 2 Overview of the Wavelet Transform...............................................................5 2.1 History of the Wavelet Transform ..................................................................5 2.2 Continuous Wavelet Transform......................................................................5 2.3 Discrete Wavelet Transform .........................................................................10 2.4 Fast Wavelet Transform................................................................................17 2.5 Choice of the Wavelet Bases ........................................................................19 2.6 Common Implementations............................................................................22 2.7 2-D Separate Wavelet Transform..................................................................25 2.8 Setup of Compression Problems...................................................................26 2.9 Setup of Denoising Problems .......................................................................28 2.10 Disadvantages of Separable Wavelets ..........................................................29 2.11 Overview of Bandelets, Curvelets, and Contourlets.....................................31 2.11.1 Bandelets .............................................................................................31 2.11.2 Curvelets .............................................................................................31 viii 2.11.3 Contourlets ..........................................................................................32 Chapter 3 Bandelet Transform ......................................................................................33 3.1 Overview.......................................................................................................33 3.2 Experiments ..................................................................................................37 Chapter 4 Curvelet Transform.......................................................................................41 4.1 Overview.......................................................................................................41 4.2 Implementation Issues ..................................................................................46 4.2.1 USFFTs................................................................................................46 4.2.2 Wrapping.............................................................................................47 4.3 Experiments ..................................................................................................50 Chapter 5 Contourlet Transform ...................................................................................55 5.1 Overview.......................................................................................................55 5.2 Implementation Issues ..................................................................................58 5.2.1 McClellan Transform..........................................................................58 5.3 Experiments ..................................................................................................60 Chapter 6 Shearlet Transform and Applications............................................................67 6.1 Overview.......................................................................................................67 6.2 Shearlet Multiresolution Analysis.................................................................68 6.2.1 Adaptive Subdivision Schemes...........................................................69 6.2.2 Shearlet Multiresolution Analysis .......................................................72 6.3 Implementation Issues ..................................................................................73 6.4 Other wavelet-like transform........................................................................74 6.4.1 Fresnelets.............................................................................................74 6.4.2 Wedgelets ............................................................................................75 6.4.3 Brushlets..............................................................................................75 ix 6.5 Applications..................................................................................................75 6.5.1 Recognition .........................................................................................77 6.5.2 Image Fusion.......................................................................................78 6.5.3 Digital Watermarking..........................................................................80 Chapter 7 Theoretical Improvement..............................................................................83 7.1 Another approach to Construct Filter Banks ................................................83 7.2 Derivation of 2D wavelet transform.............................................................84 7.3 Construction of the Wavelet Filters ..............................................................88 Chapter 8 Practical Improvement..................................................................................93 8.1 Simple Observation ......................................................................................93 8.2 Proposed Algorithm......................................................................................94 8.2.1 Adaptive Segmentation .......................................................................94 8.2.2 Details of Step 2 and Step 3 ................................................................98 8.3 Discussion and Conclusions .......................................................................110 Chapter 9 Experiments ................................................................................................111 9.1 Comprehensive Comparison.......................................................................111 9.2 Simulation of Twisted Image......................................................................113 Chapter 10 Conclusions and Future Works ...................................................................115 10.1 Conclusions ................................................................................................115 10.2 Future Works...............................................................................................116 REFERENCE ................................................................................................................118 | |
| dc.language.iso | en | |
| dc.subject | 小波轉換 | zh_TW |
| dc.subject | 多重解析分析 | zh_TW |
| dc.subject | contourlet transform | en |
| dc.subject | multiresolution analysis | en |
| dc.subject | wavelet transform | en |
| dc.subject | bandelet transform | en |
| dc.subject | curvelet transform | en |
| dc.title | 一般化二維小波轉換及影像多重解析分析 | zh_TW |
| dc.title | Multiresolution Analysis for Image by Generalized 2-D Wavelets | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 96-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 郭景明,葉敏宏,曾易聰 | |
| dc.subject.keyword | 多重解析分析,小波轉換, | zh_TW |
| dc.subject.keyword | multiresolution analysis,wavelet transform,bandelet transform,curvelet transform,contourlet transform, | en |
| dc.relation.page | 123 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2008-06-15 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
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