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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 丁建均 | zh_TW |
| dc.contributor.advisor | Jian-Jiun Ding | en |
| dc.contributor.author | 莫明勳 | zh_TW |
| dc.contributor.author | Ming-Hsun Mo | en |
| dc.date.accessioned | 2025-08-14T16:24:22Z | - |
| dc.date.available | 2025-08-15 | - |
| dc.date.copyright | 2025-08-14 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-28 | - |
| dc.identifier.citation | [1] C. Kervrann, J. Boulanger, and P. Coupe, “Bayesian non-local means filter, image redundancy and adaptive dictionaries for noise removal,” in Proc. Int. Conf. Scale Space Methods Variational Methods Comput. Vis., 2007, pp. 520–532.
[2] M. Dai, C. Peng, A. K. Chan, and D. Loguinov, “Bayesian wavelet shrinkage with edge detection for SAR image despeckling,” IEEE Trans. Geosci. Remote Sens., vol. 42, no. 8, pp. 1642–1648, Aug. 2004. [3] J. S. Lee, J. H. Wen, T. L. Ainsworth, K. S. Chen, and A. J. Chen, “Improved sigma filter for speckle filtering of SAR imagery,” IEEE Trans. Geosci. Remote Sens., vol. 47, no. 1, pp. 202–213, Jan. 2009. [4] H. Zhong, Y. W. Li, and L. C. Jiao, “Bayesian nonlocal means filter for SAR image despeckling,” in Proc. Asia-Pacific Conf. Synthetic Aperture Radar, Xian, China, Oct. 2009, pp. 1096–1099 [5] J. A. Buades, B. Coll, and J. M. Morel, “A review of image denoising algorithms, with a new one,” SIAM Interdisc. J.: Multiscale Model. Simul., vol. 4, no. 2, pp. 490–530, 2005. [6] Fan Zhang, Fei Ma, Yongsheng Zhou. A Benchmark Sentinel-1 SAR Dataset for Airport Detection (SAR-Airport-1.0)[OL]. Journal of Radars, 2024. https://radars.ac.cn/web/data/getData?dataType=SAR-Airport. [7] M. Miao, Z. Xue and P. Zhao, "A Blind Estimation for Speckle Noise Based on Gaussian-Hermite Moments," 2016 International Symposium on Computer, Consumer and Control (IS3C), Xi'an, China, 2016, pp. 829-832, doi: 10.1109/IS3C.2016.211. [8] S. Intajag and S. Chitwong, "Speckle Noise Estimation with Generalized Gamma Distribution," 2006 SICE-ICASE International Joint Conference, Busan, Korea (South), 2006, pp. 1164-1167, doi: 10.1109/SICE.2006.315296. [9] D. Cozzolino, S. Parrilli, G. Scarpa, G. Poggi and L. Verdoliva, "Fast Adaptive Nonlocal SAR Despeckling," in IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 2, pp. 524-528, Feb. 2014, doi: 10.1109/LGRS.2013.2271650. [10] K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, "Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering," in IEEE Transactions on Image Processing, vol. 16, no. 8, pp. 2080-2095, Aug. 2007, doi: 10.1109/TIP.2007.901238. [11] J. -S. Lee, "Digital Image Enhancement and Noise Filtering by Use of Local Statistics," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-2, no. 2, pp. 165-168, March 1980, doi: 10.1109/TPAMI.1980.4766994. [12] V. S. Frost, J. A. Stiles, K. S. Shanmugan and J. C. Holtzman, "A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-4, no. 2, pp. 157-166, March 1982 [13] Guozhong Chen, Xingzhao Liu and Zhixin Zhou, "Modified frost speckle filter based on anisotropic diffusion," 2007 IET International Conference on Radar Systems, Edinburgh, UK, 2007, pp. 1-4, doi: 10.1049/cp:20070566. [14] J. Wu, F. Liu, L. Jiao, X. Zhang, H. Hao and S. Wang, "Local Maximal Homogeneous Region Search for SAR Speckle Reduction With Sketch-Based Geometrical Kernel Function," in IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 9, pp. 5751-5764, Sept. 2014, doi: 10.1109/TGRS.2013.2292081. [15] S. M. Kabir and M. I. H. Bhuiyan, "Speckle noise modeling using the Bessel K-Form PDF," 2012 7th International Conference on Electrical and Computer Engineering, Dhaka, Bangladesh, 2012, pp. 268-271, doi: 10.1109/ICECE.2012.6471537. [16] S. Intajag and S. Chitwong, "Speckle Noise Estimation with Generalized Gamma Distribution," 2006 SICE-ICASE International Joint Conference, Busan, Korea (South), 2006, pp. 1164-1167, doi: 10.1109/SICE.2006.315296. [17] M. Miao, Z. Xue and P. Zhao, "A Blind Estimation for Speckle Noise Based on Gaussian-Hermite Moments," 2016 International Symposium on Computer, Consumer and Control (IS3C), Xi'an, China, 2016, pp. 829-832, doi: 10.1109/IS3C.2016.211. [18] M. V. Perera, N. G. Nair, W. G. C. Bandara and V. M. Patel, "SAR Despeckling Using a Denoising Diffusion Probabilistic Model," in IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, 2023, Art no. 4005305, doi: 10.1109/LGRS.2023.3270799. [19] H. Zhong, Y. Li and L. Jiao, "SAR Image Despeckling Using Bayesian Nonlocal Means Filter With Sigma Preselection," in IEEE Geoscience and Remote Sensing Letters, vol. 8, no. 4, pp. 809-813, July 2011, doi: 10.1109/LGRS.2011.2112331. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98513 | - |
| dc.description.abstract | 合成孔徑雷達（Synthetic Aperture Radar, SAR）影像由於雷達訊號的同調特性，固有地存在著乘性散斑雜訊，嚴重降低影像品質並影響後續影像分析與應用。傳統去噪技術雖具計算效率，但往往忽略結構與紋理資訊，導致無法有效地保留影像細節。此外，多數現有的雜訊估測方法依賴特定的參數分布假設或均質區域選取，在面對紋理複雜、結構多變的SAR場景時，這些假設難以成立，因而顯著降低了估測準確性。
本論文提出了一種基於貝葉斯非局部平均（Bayesian Nonlocal Means, BNLM）濾波器與草圖結構先驗（sketch-based geometric priors）的SAR影像散斑雜訊高效估測與抑制方法。在雜訊估測方面，研究結合了離散小波轉換（Discrete Wavelet Transform, DWT）、適應性局部變異數估測與多項式迴歸，成功地將雜訊估測的平均誤差大幅降低至2.66%，顯著優於傳統的Gaussian-Hermite方法與Generalized Gamma Distribution方法。 在雜訊抑制方面，我們所提出的改良型BNLM框架包含兩項關鍵改進：(1)以Wiener filter為基礎之先驗估測方法，能有效降低估測偏差並更精確地反映局部變異數 (2)透過草圖結構導引之異向性高斯核函數，能更精準地保留影像中的邊緣與紋理結構，本研究所提出之方法在量化指標PSNR、SSIM、運算效率上均有明顯的提升。 總體來說，本研究所提出之方法，在公開的SAR機場資料集上進行的實驗驗證顯示，在SAR影像散斑雜訊估測與去噪中的準確性、視覺真實性及運算效率上皆有顯著提升，未來更可廣泛應用於衛星影像即時處理等多元化遙測領域。 | zh_TW |
| dc.description.abstract | Synthetic Aperture Radar (SAR) imagery inherently suffers from multiplicative speckle noise, which significantly deteriorates image quality and complicates subsequent analysis tasks. Conventional denoising approaches typically overlook critical structural and textural details, while prevalent noise estimation techniques are constrained by oversimplified parametric assumptions, leading to suboptimal accuracy in complex SAR scenes.
This study presents a robust framework for SAR speckle noise estimation and suppression, integrating Bayesian Nonlocal Means (BNLM) filtering with sketch-based geometric priors. In the noise estimation phase, a combination of Discrete Wavelet Transform (DWT), adaptive local variance estimation, and polynomial regression markedly reduces the estimation error to approximately 2.66%, surpassing conventional methodologies. For noise suppression, the proposed enhanced BNLM method incorporates a Wiener filter-based prior to accurately model local variance and employs an anisotropic Gaussian kernel driven by geometric structural cues. Empirical evaluations utilizing public SAR datasets demonstrate substantial enhancements in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and computational efficiency, underscoring the method's applicability for real-time satellite imagery processing. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-14T16:24:22Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-14T16:24:22Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES vii Chapter 1 Introduction 1 Chapter 2 Related Work 3 2.1 Noise Estimation 3 2.1.1 Estimation Using Bessel K-form PDF[16] 3 2.1.2 Estimation Using Generalized Gamma Distribution[17] 4 2.1.3 Estimation Based on Gaussian-Hermite Moments[18] 6 2.2 Noise Removal 7 2.2.1 Lee Filter[12] 7 2.2.2 Frost Filter[13][14] 9 2.2.3 SAR-BM3D [10][11] 10 2.2.4 Bayesian Nonlocal Means Filter[20] 13 2.2.5 Diffusion Probabilistic Model[19] 15 Chapter 3 Proposed Noise Estimation 16 3.1 Speckle Noise Model 16 3.2 Methodology 16 3.2.1 Log-Transform 17 3.2.2 Discrete Wavelet Transform 18 3.2.3 Local Variance Estimation and Accumulation 19 3.2.4 Final Noise Estimation Using Polynomial Regression 21 3.3 Experiment and Results 22 3.3.1 Evaluation Metric 22 3.3.2 Experimental Results of Speckle Noise Estimation 23 3.3.3 Summary of Experimental Findings 26 Chapter 4 Proposed Speckle Noise Removal 27 4.1 A New Form BNL Filter 27 4.2 A Wiener-based Prior Estimation 31 4.3 Improved Preselection Based on the Sigma Range 32 4.4 A Sketch‐Based Structural Prior 33 4.4.1 Sketch Map Extraction 34 4.4.2 Anisotropic Gaussian Kernel 35 4.5 Experimental Results 36 4.5.1 Quantitative Metrics 37 4.5.2 Visual Results 38 4.5.3 Processing Time and Efficiency 45 4.5.4 Summary of Experimental Findings 46 Chapter 5 Conclusion 47 REFERENCE 49 | - |
| dc.language.iso | en | - |
| dc.subject | 散斑雜訊 | zh_TW |
| dc.subject | 合成孔徑雷達 | zh_TW |
| dc.subject | 結構先驗 | zh_TW |
| dc.subject | 雜訊估測 | zh_TW |
| dc.subject | 貝葉斯非局部平均法 | zh_TW |
| dc.subject | Bayesian Nonlocal Means | en |
| dc.subject | noise estimation | en |
| dc.subject | structural priors | en |
| dc.subject | speckle noise | en |
| dc.subject | Synthetic Aperture Radar | en |
| dc.title | 基於結構幾何先驗與貝葉斯非局部平均法之SAR影像散斑雜訊高效估計與抑制 | zh_TW |
| dc.title | Efficient Speckle Noise Estimation and Reduction for SAR Imagery Using Bayesian Nonlocal Means with Sketch-Based Geometric Priors | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 簡鳳村;盧奕璋;歐陽良昱 | zh_TW |
| dc.contributor.oralexamcommittee | Feng-Tsun Chien;Yi-Chang Lu;Liang-Yu Ou Yang | en |
| dc.subject.keyword | 合成孔徑雷達,散斑雜訊,貝葉斯非局部平均法,雜訊估測,結構先驗, | zh_TW |
| dc.subject.keyword | Synthetic Aperture Radar,speckle noise,Bayesian Nonlocal Means,noise estimation,structural priors, | en |
| dc.relation.page | 51 | - |
| dc.identifier.doi | 10.6342/NTU202502541 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-07-29 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電信工程學研究所 | - |
| dc.date.embargo-lift | 2025-08-15 | - |
| 顯示於系所單位： | 電信工程學研究所 | |
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