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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 劉俊麟 | zh_TW |
| dc.contributor.advisor | Chun-Lin Liu | en |
| dc.contributor.author | 馮小純 | zh_TW |
| dc.contributor.author | Hsiao-Chun Feng | en |
| dc.date.accessioned | 2024-08-01T16:17:24Z | - |
| dc.date.available | 2024-08-02 | - |
| dc.date.copyright | 2024-08-01 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-07-29 | - |
| dc.identifier.citation | 英文文獻
I. Pitas, Digital Image Processing Algorithms and Applications. John Wiley & Sons, 2000. M. Jing, B. W. Scotney, S. A. Coleman, and M. T. McGinnity, “The application of social media image analysis to an emergency management system,” in 2016 11th International Conference on Availability, Reliability and Security (ARES), Salzburg, Austria, 2016, pp. 805–810. J. Chen, K. Venkataraman, D. Bakin, B. Rodricks, R. Gravelle, P. Rao, and Y. Ni, “Digital camera imaging system simulation,” IEEE Transactions on Electron Devices, vol. 56, no. 11, pp. 2496 – 2505, 2009. F. Ritter, T. Boskamp, A. Homeyer, H. Laue, M. Schwier, F. Link, and H.-O. Peitgen, “Medical image analysis,” IEEE Pulse, vol. 2, no. 6, pp. 60 – 70, 2011. B. Goyal, A. Dogra, S. Agrawal, B. S. Sohi, and A. Sharma, “Image denoising review: From classical to state-of-the-art approaches,” Information Fusion, vol. 55, pp. 220 – 244, 2020. R. C. Gonzalez and R. E. Woods, Digital Image Processing. Pearson FT Press, 2017. P. Satti, N. Sharma, and B. Garg, “Min-max average pooling based filter for impulse noise removal,” IEEE Signal Processing Letters, vol. 27, pp. 1475 – 1479, 2020. W. K. Pratt, Digital Image Processing: PIKS Scientific Inside. Wiley-Interscience, 2007, vol. 4. R. Abiko and M. Ikehara, “Blind denoising of mixed gaussian-impulse noise by single cnn,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May. 2019, pp. 1717 – 1721. C. Tian, L. Fei, W. Zheng, Y. Xu, W. Zuo, and C.-W. Lin, “Deep learning on image denoising: An overview,” Neural Networks, vol. 131, pp. 251 – 275, 2020. C. Tian, Y. Xu, L. Fei, and K. Yan, “Deep learning for image denoising: A survey,” in Genetic and Evolutionary Computing: Proceedings of the Twelfth International Conference on Genetic and Evolutionary Computing, December 14-17, Changzhou, Jiangsu, China, vol. 12. Springer Singapore, 2019, pp. 563 – 572. F.-A. Croitoru et al., “Diffusion models in vision: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 9, pp. 10 850 – 10 869, 2023. R. Aboulaich, D. Meskine, and A. Souissi, “New diffusion models in image processing,” Computers & Mathematics with Applications, vol. 56, no. 4, pp. 874–882, 2008. Z. Chen and et al., “Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges,” Journal of Digital Imaging, vol. 36, no. 1, pp. 204 – 230, 2023. V. Lempitsky, A. Vedaldi, and D. Ulyanov, “Deep image prior,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2018, pp. 9446–9454. F. A. KhoKhar et al., “A review on federated learning towards image processing,” Computers and Electrical Engineering, vol. 99, p. 107818, 2022. H. Boche, A. Fono, and G. Kutyniok, “Limitations of deep learning for inverse problems on digital hardware,” IEEE Transactions on Information Theory, vol. 69, no. 12, pp. 7887 – 7908, 2023. G. Yuan and B. Ghanem, “ℓ0 TV: A sparse optimization method for impulse noise image restoration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 2, pp. 352 – 364, 2017. S. Selvin, S. G. Ajay, B. G. Gowri, V. Sowmya, and K. P. Soman, “ℓ1 trend filter for image denoising,” Procedia Computer Science, vol. 93, pp. 495 – 502, 2016. S. J. Kim, K. Koh, S. Boyd, and D. Gorinevsky, “ℓ1 trend filtering,” SIAM Review, vol. 51, no. 2, pp. 339 – 360, 2009. S. Izadi, D. Sutton, and G. Hamarneh, “Image denoising in the deep learning era,” Artificial Intelligence Review, vol. 56, no. 7, pp. 5929 – 5974, 2023. R. H. Chan, C.-W. Ho, and M. Nikolova, “Salt-and-pepper noise removal by mediantype noise detectors and detail-preserving regularization,” IEEE Transactions on Image Processing, vol. 14, no. 10, pp. 1479–1485, 2005. K. Panetta, L. Bao, and S. Agaian, “A new unified impulse noise removal algorithm using a new reference sequence-to-sequence similarity detector,” IEEE Access, vol. 6, pp. 37 225 – 37 236, 2018. G. George et al., “A survey on various median filtering techniques for removal of impulse noise from digital image,” in 2018 Conference on Emerging Devices and Smart Systems (ICEDSS). IEEE, 2018. J. Astola, P. Haavisto, and Y. Neuvo, “Vector median filters,” Proceedings of the IEEE, vol. 78, no. 4, pp. 678 – 689, 1990. R. Lukac, “Adaptive vector median filtering,” Pattern Recognition Letters, vol. 24, no. 12, pp. 1889 – 1899, 2003. M. Mafi, H. Rajaei, M. Cabrerizo, and M. Adjouadi, “A robust edge detection approach in the presence of high impulse noise intensity through switching adaptive median and fixed weighted mean filtering,” IEEE Transactions on Image Processing, vol. 27, no. 11, pp. 5475 – 5490, 2018. S. Ghosh, A. Dasgupta, and A. Swetapadma, “A study on support vector machine based linear and non-linear pattern classification,” in 2019 International Conference on Intelligent Sustainable Systems (ICISS). IEEE, February 2019, pp. 24–28. H. Yu and S. Kim, “SVM tutorial-classification, regression and ranking,” Handbook of Natural Computing, vol. 1, pp. 479–506, 2012. C. J. Burges, “A tutorial on support vector machines for pattern recognition,” Data mining and knowledge discovery, vol. 2, no. 2, pp. 121 – 167, 1998. C. Saravanan, “Color image to grayscale image conversion,” in 2010 Second International Conference on Computer Engineering and Applications. IEEE, 2010, pp. 196 – 199. P. M. Hubel, J. Liu, and R. J. Guttosch, “Spatial frequency response of color image sensors: Bayer color filters and foveon x3,” in Sensors and Camera Systems for Scientific, Industrial, and Digital Photography Applications V, vol. 5301. SPIE, June 2004, pp. 402 – 407. R. Lukac, K. N. Plataniotis, and D. Hatzinakos, “Color image zooming on the bayer pattern,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 15, no. 11, pp. 1475–1492, 2005. D. Wang et al., “Image demosaicking for bayer-patterned cfa images using improved linear interpolation,” in 2017 Seventh International Conference on Information Science and Technology (ICIST). IEEE, 2017. N. I. Petrovic and V. Crnojevic, “Universal impulse noise filter based on genetic programming,” IEEE Transactions on Image Processing, vol. 17, no. 7, pp. 1109–1120, 2008. H. M. Ali, “A new method to remove salt & pepper noise in magnetic resonance images,” in 2016 11th International Conference on Computer Engineering & Systems (ICCES). IEEE, 2016. C.-T. Lu et al., “Removal of salt-and-pepper noise for x-ray bio-images using pixelvariation gain factors,” Computers & Electrical Engineering, vol. 71, pp. 862–876, 2018. L. Bar, A. Brook, N. Sochen, and N. Kiryati, “Deblurring of color images corrupted by impulsive noise,” IEEE Transactions on Image Processing, vol. 16, no. 4, pp. 1101 – 1111, 2007. M. Sonka, V. Hlavac, and R. Boyle, Image processing, analysis and machine vision. Springer, 2013. H. Tang, A. Ortis, and S. Battiato, “The impact of padding on image classification by using pre-trained convolutional neural networks,” in Image Analysis and Processing–ICIAP 2019: 20th International Conference, Trento, Italy, September 9–13, 2019, Proceedings, Part II, vol. 20. Springer International Publishing, 2019, pp. 337 – 344. L. G. Hamey, “A functional approach to border handling in image processing,” in 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, November 2015, pp. 1–8. A.-D. Nguyen et al., “Distribution padding in convolutional neural networks,” in 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019. C. M. Bishop, Pattern Recognition and Machine Learning. Springer, 2006. D. N. H. Thanh and S. Engínoğlu, “An iterative mean filter for image denoising,” IEEE Access, vol. 7, pp. 167 847–167 859, 2019. M. R. Lone and E. Khan, “A good neighbor is a great blessing: nearest neighbor filtering method to remove impulse noise,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 10, pp. 9942 – 9952, 2022. C. Guillemot and O. L. Meur, “Image inpainting: Overview and recent advances,” IEEE Signal Processing Magazine, vol. 31, no. 1, pp. 127–144, 2013. M. Bertalmio et al., “Image inpainting,” in Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques. ACM, 2000. S. Perreault and P. Hébert, “Median filtering in constant time,” IEEE Transactions on Image Processing, vol. 16, no. 9, pp. 2389 – 2394, 2007. H. Hwang and R. A. Haddad, “Adaptive median filters: new algorithms and results,” IEEE Transactions on Image Processing, vol. 4, no. 4, pp. 499 – 502, 1995. R. Caruana and A. Niculescu-Mizil, “An empirical comparison of supervised learning algorithms,” in Proceedings of the 23rd international conference on Machine learning, June 2006, pp. 161 – 168. Y. Wendong, L. Zhengzheng, and J. Bo, “A multi-factor analysis model of quantitative investment based on GA and SVM,” in 2017 2nd International Conference on Image, Vision and Computing (ICIVC). IEEE, June 2017, pp. 1152 – 1155. M. Somvanshi, P. Chavan, S. Tambade, and S. V. Shinde, “A review of machine learning techniques using decision tree and support vector machine,” in 2016 International Conference on Computing Communication Control and Automation (ICCUBEA). IEEE, August 2016, pp. 1 – 7. K. P. Soman, R. Loganathan, and V. Ajay, Machine learning with SVM and other kernel methods. PHI Learning Pvt. Ltd., 2009. C. Robert, Machine Learning, a Probabilistic Perspective. The MIT Press, 2014. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93469 | - |
| dc.description.abstract | 在影像處理中，影像捕捉期間的感測器故障以及數位類比轉換器的故障會導致椒鹽雜訊。椒鹽雜訊將隨機極值引入影像像素，這可能會降低影像品質。因此，我們應該在其他影像處理任務之前進行影像去噪，以確保後續操作的準確性。目前，椒鹽雜訊去噪的方法主要有三種：機器學習、變分方法和基於中位數的濾波器。機器學習需要大量訓練資料來學習影像特徵，但隱私和資料缺乏可能會成為問題。變分方法表現出優異的去噪性能，但計算複雜度高，參數選擇困難。然而，基於中位數的濾波器不需要大量的訓練數據，且計算複雜度較低。儘管如此，濾波器的降噪性能可能會受到雜訊參數的影響。因此，我們研究我們的新穎演算法。
我們提出了用於椒鹽去雜訊的「Hue-SVM Switch Adaptive Median-Based Filter」。我們對自適應中值濾波器進行調整以進行彩色影像去噪。我們利用HSI顏色模型和SVM分類器在我們提出的兩個基於中值的濾波器之間自動切換。我們的濾波器可以根據雜訊參數靈活選擇演算法。此外，與其他基於中值的濾波器相比，我們的濾波器可以實現更高品質的去噪影像。在模擬結果中，我們的濾波器在不同的雜訊參數下展現了最佳的去噪能力。 | zh_TW |
| dc.description.abstract | In image processing, sensor malfunctions during image capture and faults in the digital-to-analog converter result in salt-and-pepper noise. Salt-and-pepper noise introduces random extreme values into image pixels, which may decrease image quality. Therefore, we should perform image denoising before other image processing tasks to ensure the accuracy of subsequent operations. Currently, there are three main approaches for denoising salt-and-pepper noise: machine learning, variational methods, and median-based filters. Machine learning requires a lot of training data to learn image features, but privacy and a lack of data can be problems. Variational methods exhibit excellent denoising performance but come with high computational complexity and difficulty in parameter selection. However, median-based filters do not require a lot of training data and have low computational complexity. Nonetheless, the denoising performance of filters can be affected by noise parameters. Therefore, we research our novel algorithms.
We propose the Hue-SVM Switch Adaptive Median-Based Filter for salt-and-pepper denoising. We make adjustments to the adaptive median filter for color image denoising. We utilize the HSI color model and SVM classifier for automatic switching between the two median-based filters that we propose. Our filter can flexibly select algorithms based on noise parameters. Moreover, our filter achieves higher-quality denoised images compared to other median-based filters. In the simulation results, our filter demonstrates the best denoising capability across different noise parameters. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-01T16:17:24Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-01T16:17:24Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Verification Letter from the Oral Examination Committee i
Acknowledgements iii 摘要 v Abstract vii Table of Contents ix List of Figures xiii List of Tables xvii Chapter 1 Introduction 1 1.1 Overview and Motivation 1 1.2 Outline of the Thesis 6 1.3 Notations 6 Chapter 2 Preliminaries 9 2.1 Fundamentals of Image Modeling 9 2.1.1 Grayscale Image Model 9 2.1.2 RGB Color Image Model 10 2.1.3 HSI Color Image Model 12 2.2 Salt-and-Pepper Noise 14 2.2.1 Grayscale Image Salt-and-Pepper Noise Model 15 2.2.2 Independent channels independent location (ICIL) Model 16 2.2.3 Dependent channels independent location (DCIL) Model 19 2.3 Padding Strategies 23 2.3.1 Zero Padding 24 2.3.2 Mirror Padding 25 2.3.3 Replicate Padding 27 2.4 Windows in Filtering Method 29 2.4.1 General Square Window 29 2.4.2 Neighbor Window 31 2.4.3 Noise-Free Window 34 Chapter 3 Review of Common Salt-and-Pepper Noise Removal Filters 39 3.1 Median Filter (MF) 40 3.2 Adaptive Median Filter (AMF) 43 3.3 Vector Median Filter (VMF) 48 3.4 Adaptive Vector Median Filter (AVMF) 55 3.5 Adaptive Median Filter-Based Method (SAMF switch 1) 61 Chapter 4 Algorithms of Switch Adaptive Median-Based Filter 67 4.1 Proposed Algorithms 67 4.1.1 Adaptive Neighbor Window-Based Median Filter (ANWMF) 68 4.1.2 Multi-Case Adaptive Median-Based Filter (MCAMBF) 72 4.2 Simulation of the Salt-and-Pepper Denoising 82 4.2.1 Simulated Scenario 82 4.2.2 Simulation Parameter Configuration 83 4.2.3 Even Noise Scatter (ICIL Noise Model) 85 4.2.4 High Full-Channel Damage (DCIL Noise Model with High p1) 93 4.2.5 Single Channel Intact (DCIL Noise Model with High p2) 100 4.3 Summary of Algorithms and Simulation Results 107 Chapter 5 Switch Condition 109 5.1 The Classification Step Based on the HSI Color Space 110 5.1.1 Classification Based on the Standard Deviation of H 113 5.1.2 Standard Deviation of the H values for Noisy Images 117 5.1.3 Steps of the Classification Process Based on H Standard Deviation 120 5.2 Support Vector Machine Classification 120 5.2.1 The Fundamental Concept of SVM Binary Classifier 121 5.2.2 Mathematical Model of Hard Margin SVM Classifier 123 5.2.3 Generating Data Points for The SVM Classification 126 5.2.4 Hard Margin SVM Classifier in the Switch Filter 128 5.2.4.1 Training the Hard Margin SVM Classifier 128 5.2.4.2 Accuracy of the Hard Margin SVM Classifier with the Testing Data 130 5.3 Switch Condition Process 133 5.4 Conclusion of Switch Condition 138 Chapter 6 Conclusion and Future Work 139 References 141 Appendix A — Derivation of Experimental Parameters for DCIL Noise Model 149 Appendix B — Parameters Setting of Data Points for the SVM Classification 151 | - |
| dc.language.iso | en | - |
| dc.subject | 去噪 | zh_TW |
| dc.subject | 椒鹽噪聲 | zh_TW |
| dc.subject | 彩色影像 | zh_TW |
| dc.subject | 基於自適應切換中值濾波器 | zh_TW |
| dc.subject | 支持向量機 | zh_TW |
| dc.subject | 硬邊界 | zh_TW |
| dc.subject | 色調值 | zh_TW |
| dc.subject | Denoising | en |
| dc.subject | Hue value | en |
| dc.subject | hard margin | en |
| dc.subject | SVM | en |
| dc.subject | switch adaptive median filter-based | en |
| dc.subject | color image | en |
| dc.subject | salt-and-pepper noise | en |
| dc.title | 色調及SVM的開關自適應中值濾波器用於鹽和胡椒去噪 | zh_TW |
| dc.title | Hue-SVM Switch Adaptive Median-Based Filter for Salt-and-Pepper Denoising | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林家祥;丁建均 | zh_TW |
| dc.contributor.oralexamcommittee | Chia-Hsiang Lin;Jian-Jiun Ding | en |
| dc.subject.keyword | 去噪,椒鹽噪聲,彩色影像,基於自適應切換中值濾波器,支持向量機,硬邊界,色調值, | zh_TW |
| dc.subject.keyword | Denoising,salt-and-pepper noise,color image,switch adaptive median filter-based,SVM,hard margin,Hue value, | en |
| dc.relation.page | 158 | - |
| dc.identifier.doi | 10.6342/NTU202401708 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2024-07-31 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電信工程學研究所 | - |
| 顯示於系所單位： | 電信工程學研究所 | |
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