以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：8

、訪客IP：3.149.255.162

姓名	趙彬(Bin Chao)  查詢紙本館藏	畢業系所	數學系
論文名稱	混合噪聲的即時圖像去噪在螢光顯微鏡圖像和古畫中的應用 (Real-Time Image Denoising of Mixed Noise in Applications of Fluorescence Microscopy Images and Ancient Paintings)
檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 ( 永不開放)
摘要(中)	影像處理中，影像去雜訊是個非常重要的研究課題，近年來，許多專家和學者已經提出非常多效果不錯的去噪方法。在實際應用上，可以利用在古畫去污、耳機的即時降噪、生物醫學影像的去噪等。然而，大部分的方法只適用於處理影像中只有一種雜訊的情形。事實上，在一些真實圖像中經常含有多個雜訊組成的混合噪聲。例如螢光顯微鏡的成像中就含有泊松和高斯的混合雜訊。此外，混合雜訊在去噪過程上也較困難。在這篇文章中，我們首先介紹 P-M 模型和分數階層模型是如何去除椒鹽雜訊。同時我們應用小波閥值、非局部平均模型和三維塊匹配算法去除高斯雜訊。接著提出我們的演算法去除混合雜訊和加入總變差去噪的加速法來縮短模型的運算時間，此外也介紹 Noise2Noise 模型的即時去噪法去除混合雜訊。最後我們展示每種方法在對各種混合雜訊進行去噪時的效果和對這些實作結果做總體比較分析。
摘要(英)	Image denoising is one of important research topics in image processing. In recent years, many scholars and experts have proposed many effective denoising methods. In practical applications, it can be used for removing dirt from ancient paintings, instant noise reduction of headphones, denoising of biomedical images, etc. However, most methods are suitable while the image contains only one type of noises. In fact, some real images often contain mixed noise composed of multiple noises. For example, the images of fluorescence microscopy contain mixed noises of Poisson and Gaussian noise. In addition, mixed noise is also more difficult to denoise. This paper first introduces how the respective P-M model and fractional order model remove salt and pepper noise. We simultaneously apply wavelet thresholding, NL-Means model and BM3D algorithm to remove Gaussian noise. Secondly, we propose our algorithm for removing mixed noise and add the accelerated method of total variation denoising to shorten the running time of the model. Furthermore, we introduce the real-time denoising method of the Noise2Noise model for removing various mixed noises. Finally, we show the effect of each method on denoising various mixed noises and make an overall comparative analysis of these implementation results.
關鍵字(中)	★ P-M 模型 ★ 分數階層模型 ★ 小波閥值 ★ 非局部平均模型 ★ 三維塊匹配算法 ★ 總變差去噪法 ★ Noise2Noise 模型	關鍵字(英)	★ P-M model ★ Fractional order model ★ Wavelet thresholding ★ NL-Means model ★ BM3D ★ Total variation denoising method ★ Noise2Noise model
論文目次	摘要 i Abstract ii Contents iii 1 Introduction 1 2 Research Context and Methods 2 2.1 Perona-Malik Anisotropic Diffusion Model (P-M Model) . . . . . . . . . . . 2 2.2 Adaptive Fractional Order Anisotropic Diffusion . . . . . . . . . . . . . . . . 3 2.2.1 Theory of Fractional Order Method and Local Entropy . . . . . . . . 3 2.2.2 Definition of Noise Points . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2.3 Selections of Different Order α . . . . . . . . . . . . . . . . . . . . . 5 2.3 Wavelet Thresholding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3.1 Hard Thresholding and Soft Thresholding . . . . . . . . . . . . . . . 6 2.3.2 Selection of Wavelet Threshold β . . . . . . . . . . . . . . . . . . . . 6 2.4 Non-Local Means Model (NL-Means Model) . . . . . . . . . . . . . . . . . . 7 2.5 Block-Matching and 3D Filtering Algorithm (BM3D) . . . . . . . . . . . . . 7 2.5.1 Step 1 : Basic Estimates . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.5.2 Step 2 : Final Estimates . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.6 Real-Time Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.6.1 Introduction to Mixed Noise Structures . . . . . . . . . . . . . . . . . 9 2.6.2 Noise2Noise Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3 Our Proposed Method and Algorithmic Approach 10 3.1 Developed Method and Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 10 3.2 Acceleration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2.1 Majorization-minimization (MM) Method . . . . . . . . . . . . . . . 11 3.2.2 Total Variation Denoising Method (TV Method) . . . . . . . . . . . 12 3.3 Implementation and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4 Research Experiments 15 4.1 Diffusion Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.2 Non-Local Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.2.1 Results of NL-Means . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.2.2 Results of BM3D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.3 Impacts of Each Model on Various Mixed Noises . . . . . . . . . . . . . . . . 19 4.3.1 Results of Noise2Noise Model . . . . . . . . . . . . . . . . . . . . . . 19 4.3.2 Various Mixed Noise Denoising Effects under Different Models . . . . 21 5 Conclusion 28 References 29
參考文獻	[1] P. Perona, and J. Malik,“Scale-Space and Edge Detection Using Anisotropic Diffusion.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, July 1990, pp. 629–39. [2] J. Yu, Y. Wang, and Y. Shen,“Noise reduction and edge detection via kernel anisotropic diffusion.”Pattern Recognition Letters, vol. 29, July 2008, pp. 1496–1503. [3] S. Morfu, “Image processing using diffusion processes.”Proceedings of 2010 IEEE International Symposium on Circuits and Systems, 2010, pp. 1811–1814. [4] M. Malarvel, G. Sethumadhavan, Purna Chandra Rao Bhagi, S. Kar, T. Saravanan, and A. Krishnan, “Anisotropic diffusion based denoising on X-radiography images to detect weld defects.”Digital Signal Processing, vol. 68, September 2017, pp. 112–126. [5] S.M. Chao, and D.M. Tsai, “An improved anisotropic diffusion model for detail- and edge-preserving smoothing.”Pattern Recognition Letters, vol. 31, October 2010, pp. 2012- 2023. [6] A. Buades, B. Coll, and J.-M. Morel,“Image denoising and enhancement based on adaptive fractional calculus of small probability strategy.”Neurocomputing, vol. 175, January 2016, pp. 704–714. [7] S.K. Chandra, and M. Kumar Bajpai, “Fractional Anisotropic Diffusion For Image Denoising.”IEEE 8th International Advance Computing Conference (IACC), December 2018, pp. 344-348. [8] S.K. Chandra, and M. Kumar Bajpai, “Image Denoising Based on Adaptive Fractional Order with Improved PM Model.”Mathematical Problems in Engineering , 2018, pp. 1-11. [9] S.K. Chandra, and M. Kumar Bajpai,“A simple and flexible modification of Grünwald– Letnikov fractional derivative in image processing.”Mathematical Sciences, vol. 12, 2018, pp. 205-210. [10] J. Yu, L. Tan, S. Zhou, L. Wang, and M. A. Siddique, “Image Denoising Algorithm Based on Entropy and Adaptive Fractional Order Calculus Operator.”IEEE access, vol. 5, 2017, pp. 12275-12285. [11] J. Zhang, R. Xiong, C. Zhao, S. Ma, and D. Zhao, “Exploiting Image Local And Nonlocal Consistency For Mixed Gaussian-Impulse Noise Removal.”IEEE International Conference on Multimedia and Expo, July 2012, pp. 592-597. [12] C. He, K. Guo, and H. Chen, “An Improved Image Filtering Algorithm for Mixed Noise.”Applied Sciences, vol. 11, issue 21, November 2021. Retrieved from https://doi.org/10.3390/app112110358 [13] A. Buades, B. Coll, and J.-M. Morel,“A non-local algorithm for image denoising.”IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 2, July 2005, pp. 60–65. [14] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering.”IEEE Trans. Image Processing, vol. 16, no. 8, August 2007, pp. 2080-2095. [15] K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, ”Color Image Denoising via Sparse 3D Collaborative Filtering with Grouping Constraint in Luminance-Chrominance Space.” 2007 IEEE International Conference on Image Processing, 2007, pp. 313-316. [16] K. Dabov, A. Foi and K. Egiazarian, ”Video denoising by sparse 3D transform-domain collaborative filtering.” 2007 15th European Signal Processing Conference, 2007, pp. 145- 149. [17] M. Figueiredo, J. Bioucas-Dias, J. P. Oliveira, and R. D. Nowak, “On total-variation denoising: A new majorization-minimization algorithm and an experimental comparison with wavelet denoising.”Proc. IEEE Int. Conf. Image Processing (ICIP), 2006, pp. 2633- 2636. [18] Ivan W. Selesnick, “Total variation denoising (an MM algorithm).”NYU Polytechnic School of Engineering Lecture Notes, 2012. [19] Ivan W. Selesnick, and I. Bayram, Total variation filtering.”White paper, vol. 11, 2010. [20] G. E. Hinton and R. Salakhutdinov, ”Reducing the dimensionality of data with neural networks.” Science, vol. 313, no. 5786, 2006, pp. 504-507. [21] V. Mannam, Y. Zhang, Y. Zhu, E. Nichols, Q. Wang, V. Sundaresan, S. Zhang, C. Smith, P.W Bohn, and S. Howard, ”Real-time Image Denoising of Mixed Poisson-Gaussian Noise in Fluorescence Microscopy Images using ImageJ.” November 2021. Retrieved from https://doi.org/10.1101/2021.11.10.468102 [22] V. Mannam, Y. Zhang, Y. Zhu, E. Nichols, Q. Wang, V. Sundaresan, S. Zhang, C. Smith, P.W Bohn, and S. Howard, ”Real-time Image Denoising of Mixed Poisson-Gaussian Noise in Fluorescence Microscopy Images using ImageJ: Supplemental document.” November 2021. Retrieved from https://doi.org/10.1101/2021.11.10.468102 [23] Ancient painting. Retrieved from https://theme.npm.edu.tw/opendata/DigitImageSets.aspx?sNo=04025906 [24] FMD dataset. Retrieved from https://curate.nd.edu/show/f4752f78z6t
指導教授	陳建隆 許正雄(Jann-Long Chern Cheng-Hsiung Hsu)	審核日期	2022-7-25
推文	facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu
網路書籤	Google bookmarks   del.icio.us   hemidemi   myshare