博碩士論文 106221013 詳細資訊




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:42 、訪客IP:3.145.74.54
姓名 楊建昱(Jian-Yu Yang)  查詢紙本館藏   畢業系所 數學系
論文名稱
(Variational Image Fusion with First-Order Gradient Information)
相關論文
★ 遲滯型細胞神經網路似駝峰行進波之研究★ 穩態不可壓縮那維爾-史托克問題的最小平方有限元素法之片狀線性數值解
★ Global Exponential Stability of Modified RTD-based Two-Neuron Networks with Discrete Time Delays★ 二維穩態不可壓縮磁流體問題的迭代最小平方有限元素法之數值計算
★ 兩種迭代最小平方有限元素法求解不可壓縮那維爾-史托克方程組之研究★ 非線性耦合動力網路的同步現象分析
★ 邊界層和內部層問題的穩定化有限元素法★ 數種不連續有限元素法求解對流佔優問題之數值研究
★ 某個流固耦合問題的有限元素法數值模擬★ 高階投影法求解那維爾-史托克方程組
★ 非靜態反應-對流-擴散方程的高階緊緻有限差分解法★ 二維非線性淺水波方程的Lax-Wendroff差分數值解
★ Numerical Computation of a Direct-Forcing Immersed Boundary Method for Simulating the Interaction of Fluid with Moving Solid Objects★ On Two Immersed Boundary Methods for Simulating the Dynamics of Fluid-Structure Interaction Problems
★ 生成對抗網路在影像填補的應用★ 非穩態複雜流體的人造壓縮性直接施力沉浸邊界法數值模擬
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]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 影像融合是影像處理領域裡的一個重要議題,它的主要目標是整合多張給定的影像形成一張比原始影像更具視覺品質的融合影像。本文的目標是改進 Li 和 Zeng 在 [14] 針對局部模糊影像所提出的變分影像融合模型。首先,我們設計一種新的選擇準則用來選取給定的多張圖像的一階梯度信息作為圖像特徵,預期該圖像特徵應該接近目標圖像的梯度向量,然後我們以所取得的圖像特徵做為基礎,提出一個變分圖像融合模型用以融合給定的局部模糊影像,並且引用分離 Bregman 迭代法有效地解決相對應的變分問題。最後,數值例子驗證了所提出的新模型的有效性,同時我們還與 Li 和 Zeng 模型 [14] 的結果進行了比較。
摘要(英) Image fusion is an important issue in the field of image processing. The main goal of image fusion is to integrate several source images into a fused image with a better visual quality compared to the source images. The purpose of this thesis is to improve the variational image fusion model for local-blurred images proposed by Li and Zeng in [14]. First, we design a new selection criterion to select the first-order gradient information of source images as the image feature which is expected to be close to the gradient of the target image feature. We then propose a variational image fusion model using this image feature to fuse the given local-blurred images. Furthermore, the split Bregman iteration is employed to efficiently solve the corresponding variational problem. Finally, numerical examples are given to illustrate the effectiveness of the newly proposed model. Comparisons are also made with the results of the Li-Zeng model [14].
關鍵字(中) ★ 影像融合
★ 特徵選取
★ 變分影像融合模型
★ 分離 Bregman 迭代
關鍵字(英) ★ image fusion
★ feature selection
★ variational image fusion model
★ split Bregman iteration
論文目次 Contents

中文摘要 ....................................................... i

英文摘要 ...................................................... ii

Contents .................................................... iii

Abstract ...................................................... 1

1 Introduction ................................................ 2

2 Variational image fusion model .............................. 4
2.1 Feature selection ........................................ 4
2.2 The proposed model ....................................... 6

3 Numerical scheme ............................................ 7
3.1 The split Bregman iteration .............................. 7
3.2 Difference operators ..................................... 8

4 Numerical experiments ....................................... 9
4.1 Test1 ................................................... 10
4.2 Test2 ................................................... 14
4.3 Test3 ................................................... 19
4.4 Test4 ................................................... 24

5 Summary and conclusion ..................................... 29

References ................................................... 30
參考文獻 [1] C. Ballester, V. Caselles, L. Igual, J. Verdera, and B. Roug´e, A variational model for P+XS image fusion, International Journal of Computer Vision, 69 (2006), pp. 43-58.

[2] N. Burgos, M. J. Cardoso, M. Modat, S. Pedemonte, J. Dickson, A. Barnes, J. S. Duncan, D. Atkinson, S. R. Arridge, B. F. Hutton, and S. Ourselin, Attenuation correction synthesis for hybrid PET-MR scanners, In: K. Mori, I. Sakuma, Y. Sato, C. Barillot, N. Navab, Editors, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, pp. 147-154, Lecture Notes in Computer Science, Vol. 8149, Springer, Berlin.

[3] R. S. Blum and Z. Liu, Multi-Sensor Image Fusion and Its Applications, CRC Press, Taylor & Francis, Boca Raton, Florida, 2005.

[4] P. J. Burt and R. J. Kolczynski, Enhanced image capture through fusion, 1993 (4th) International Conference on Computer Vision, IEEE, (1993), pp. 173–182.

[5] K. Bredies, K. Kunisch, and T. Pock, Total generalized variation, SIAM Journal on Imaging Sciences, 3 (2010), pp. 492-526.

[6] K. Bredies and T. Valkonen, Inverse problems with second-order total generalized variation constraints, In: Proceedings of Sampling Theory and Applications, 2011.

[7] A. Chambolle and P. L. Lions, Image recovery via total variation minimization and related problems, Numerische Mathematik, 76 (1997), pp.167-188.

[8] J. F. Cai, S. Osher, and Z. Shen, Split Bregman methods and frame based image restoration, Multiscale Modeling and Simulation: a SIAM Interdisciplinary Journal, 8 (2009), pp. 337-369.

[9] M. J. Ehrhardt and S. R. Arridge, Vector-valued image processing by parallel level sets, IEEE Transactions on Image Processing, 23 (2014), pp. 9-18.

[10] T. Goldstein and S. Osher, The split Bregman method for L1 regularized problems, SIAM Journal on Imaging Sciences, 2 (2009), pp. 323-343.

[11] Y. Hu and M. Jacob, Higher degree total variation (hdtv) regularization for image recovery, IEEE Transactions on Image Processing, 21 (2012), pp. 2559-2571.

[12] H. Li, B. S. Manjunath, and S. K. Mitra, Multi-sensor image fusion using the wavelet transform, Graphical Models and Image Processing, 57 (1995), pp. 235-245.

[13] M. Lysaker and X. C. Tai, Iterative image restoration combining total variation minimization and a second-order functional, International Journal of Computer Vision, 66 (2006), pp. 5-18.

[14] F. Li and T. Zeng, Variational image fusion with first and second-order gradient information, Journal of Computational Mathematics, 34 (2016), pp. 200-222.

[15] A. G. Mahyari and M. Yazdi, Panchromatic and multispectral image fusion based on maximization of both spectral and spatial similarities, IEEE Transactions on Geoscience and Remote Sensing, 49 (2011), pp. 1976-1985.

[16] G. Piella, Image fusion for enhanced visualization: a variational approach, International Journal of Computer Vision, 83 (2009), pp. 1-11.

[17] G. Pajares and J. M. de la Cruz, A wavelet-based image fusion tutorial, Pattern Recognition, 37 (2004), pp. 1855-1872.

[18] S. Setzer, Operator splittings, Bregman methods and frame shrinkage in image processing, International Journal of Computer Vision, 92 (2011), pp. 265-280.

[19] P. Scheunders and S. De Backer, Fusion and merging of multispectral images with use of multiscale fundamental forms, Journal of the Optical Society of America A, 18 (2001), pp. 2468-2477.

[20] D. A. Socolinsky and L. B. Wolff, Multispectral image visualization through first-order fusion, IEEE Transactions on Image Processing, 11 (2002), pp. 923-931.

[21] Q. Zhang and B. Guo, Multifocus image fusion using the nonsubsampled contourlet transform, Signal Processing, 89 (2009), pp. 1334-1346.
指導教授 楊肅煜(Suh-Yuh Yang) 審核日期 2019-7-29
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   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明