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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳宜良(I-Liang Chern) | |
| dc.contributor.author | Yu-Guo Liu | en |
| dc.contributor.author | 劉于國 | zh_TW |
| dc.date.accessioned | 2021-06-13T15:59:43Z | - |
| dc.date.available | 2009-07-30 | |
| dc.date.copyright | 2008-07-30 | |
| dc.date.issued | 2008 | |
| dc.date.submitted | 2008-04-30 | |
| dc.identifier.citation | [1] D Ling, H. Y. Hsu, G Lin, S. H. Lee, Enhanced image-based coordinate measurement using a super-resolution method, Robotics and Computer-Integrated Manufacturing 21 (2005), pp. 579--588.
[2] A. Chambolle, An Algorithm for Total Variation Minimization and Applications, Journal of Mathematical Imaging and Vision 20(2004), pp. 89--97. [3] F. Malgouyres, Minimizing the Total Variation Under a General Convex Constraint for Image Restoration, IEEE Trans. Image Processing, 11(2002), pp. 1450--1456. [4] Y. Li, F. Santosa, A computational algorithm for minimizing total variation in image restoration, IEEE Trans. Image Processing, 5(1996), pp. 987--995. [5] L. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms, Phys. D, 60 (1992), pp. 259--268. [6] C. R. Vogel and M. E. Oman, Iterative methods for total variation denoising, SIAM J. Sci. Comput, 17 (1996), pp. 227--238. [7] T. F. Chan, G. H. Golub, and P. Mulet, A nonlinear primal-dual method for total variationbased image restoration, SIAM J. Sci. Comput., 20 (1999), pp. 1964--1977. [8] Q. Chang, I. L. Chern, Acceleration Methods for Total Variation-Based Image Denoising, SIAM J. Sci. Comput, 25(2003), pp. 982--994. [9] Vogel C, Acar R, Analysis of bounded variation penalty methods for ill-posed problems. Inverse Problems 10 (1994) 1217-1229. Printed in the UK. [10] Zbigniew Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/38066 | - |
| dc.description.abstract | In this paper we develope a new method based on the genetic algorithm to solve the total variation-based denoising problems. First, we briefly describe the genetic algorithm and some backgrounds of the total variation-based denoising problems. Second, we start to develope the genetic algorithm for the TV-based denoising problem. Finally we show some numerical results to demonstrate this method is fast. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T15:59:43Z (GMT). No. of bitstreams: 1 ntu-97-R94221022-1.pdf: 792628 bytes, checksum: 93c0cfceef40461dc8e4d471bb7ef976 (MD5) Previous issue date: 2008 | en |
| dc.description.tableofcontents | Contents
1. Abstract 1 2. Introduction 2 3. Genetic Algorithm 4 4. Three Points Genetic Algorithm 10 4.1 Motivation 10 4.2 Algorithm 13 5. Numerical Results 15 6. A Genetic Algorithm for total variation denoising problem 18 7. Conclusion 27 8. Code 28 9. References 33 | |
| dc.language.iso | en | |
| dc.subject | 全變差 | zh_TW |
| dc.subject | 基因演算法 | zh_TW |
| dc.subject | Total Variation | en |
| dc.subject | Genetic Algorithm | en |
| dc.title | 基於全變差去雜訊的基因演算法 | zh_TW |
| dc.title | Genetic Algorithm for total variation based denoising problem | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 96-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 周謀鴻(Mou-Hong Zhou),薛克民(Ke-Min Xue) | |
| dc.subject.keyword | 基因演算法,全變差, | zh_TW |
| dc.subject.keyword | Genetic Algorithm,Total Variation, | en |
| dc.relation.page | 33 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2008-05-05 | |
| dc.contributor.author-college | 理學院 | zh_TW |
| dc.contributor.author-dept | 數學研究所 | zh_TW |
| 顯示於系所單位： | 數學系 | |
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|---|---|---|---|
| ntu-97-1.pdf 未授權公開取用 | 774.05 kB | Adobe PDF |
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