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系統識別號 U0026-0812200910393363
論文名稱(中文) 以氫氧化鈣再生煙道氣中二氧化碳吸收劑-氨水溶液之研究
論文名稱(英文) The study of the regeneration of NH3 solution by Ca(OH)2 for CO2 absorbed in the flue gas
校院名稱 成功大學
系所名稱(中) 環境工程學系碩博士班
系所名稱(英) Department of Environmental Engineering
學年度 91
學期 2
出版年 92
研究生(中文) 黃騰德
學號 p5690128
學位類別 碩士
語文別 中文
口試日期 2003-06-06
論文頁數 126頁
口試委員 口試委員-張仁瑞
口試委員-李文智
口試委員-鄧熙聖
指導教授-朱信
關鍵字(中) 吸收容量
再生效率
氨水溶液
二氧化碳
關鍵字(英) Regeneration efficiency
Ammonia solution
Carbon dioxide
Absorption capac
學科別分類
中文摘要 於二氧化碳分離與回收技術中,以化學溶劑吸收法研究最多,也被認為最經濟可行,於實驗室研究中發現,以氨水吸收二氧化碳的吸收效果佳。本研究中主要是利用半連續式的噴霧塔反應器來吸收二氧化碳,並探討再生吸收二氧化碳至飽和之氨水溶液,及其再生的效率與吸收容量之變化。研究成果分述如下:
(1) 以不同的氨水濃度,加入氫氧化鈣以進行再生反應,研究中發現以1%氨水溶液,其再生效果最佳可達68.39%,其次為3%與5%之氨水溶液。
(2) 以不同的氫氧化鈣加藥量進行再生,發現以Ca(OH)2/CO2=1.5/1、1/1、0.75/1(莫耳比),不同之計量比下進行再生實驗,發現當計量比Ca(OH)2/CO2=1.5/1、1/1時,並無明顯之差異性,而其再生效率及吸收容量均較計量比Ca(OH)2/CO2 = 0.75/1為高。
(3) 以不同的再生時間下進行再生,並分別於密閉之系統中進行攪拌再生及不攪拌再生,由結果顯示,利用攪拌器再生,其再生反應較佳,於進行十分鐘後已達穩定。
(4) 以氫氧化鈣再生循環吸收四次,發現隨著再生次數的增加,氨水之吸收容量呈現出一定之衰退性,使得氨水溶液之吸收容量由1.67降至0.27 kgCO2/kgNH3。
(5) 分別以氧化鈣與氫氧化鈣再生氨水吸收液,發現以氫氧化鈣再生氨水溶液之吸收容量與再生效率較以氧化鈣再生效果佳。
(6) 我們可由氨水實驗所得之CO2吸收容量進行推估吸收之反應所產生之產物應以碳酸銨為主,而非碳酸氫銨。
英文摘要 Among these techniques of various technologies have been tested to remove and recover CO2 from flue gas streams. Chemical solvent absorption methods have been extensively studied and are considered as a reliable and relatively low cost method for reducing CO2. The process of ammonia scrubbing is a promising technology in reducing the greenhouse effect.
This study was conducted a semi-continuous flow experiments to absorb the CO2 gas in the spray tower reactor. To determine the absorption capacity and the regeneration efficiency after regenerated the absorbed NH3 solution. The explanation of results can be divided into five major parts.
(1) The effect of the NH3 solution concentration: we can find the regeneration efficiency using Ca(OH)2 to regenerate the absorbed NH3 solution, and the maximum regeneration efficiency can achieve 68.39% under 1% NH3 solution.
(2) The effect of the mole ratio of Ca(OH)2/CO2: we can find the regeneration experiment under the different mole ratio of Ca(OH)2/CO2 = 1.5/1, 1/1, 0.75/1, the result shows the mole ratio of Ca(OH)2/CO2 = 1.5/1, 1/1, it is no significant changes about the absorption capacity and the regeneration efficiency,but is higher than the mole ratio Ca(OH)2/CO2 = 0.75/1.
(3) The effect of the regeneration time: the result shows the efficiency of regeneration happened in the agitated vessel reactor is higher than that reacted under 10 min in the close system.
(4) The absorption capacity the NH3 solution decreases after regenerated four times,and the result shows the absorption capacity decreases from 1.67 to 0.27 kgCO2/kgNH3.
(5) The Ca(OH)2 have the higher absorption capacity and the regeneration efficiency among Ca(OH)2 and CaO reagent to regenerate NH3 solution.
(6) We can predict the main production of the reaction between CO2 and ammonia is (NH4)2CO3 from accounting the absorption capacity.
論文目次 誌謝 I
摘要 I
Abstract II
總目錄 IV
表目錄 VII
圖目錄 VIII
第一章 緒論 1
1-1 研究動機 1
1-2 研究內容與架構 4
第二章 文獻回顧 6
2-1二氧化碳的來源、特性及溫室效應對環境的影響 6
2-1-1二氧化碳的來源、特性及國內現況 6
2-1-2溫室效應對環境的影響 7
2-2 二氧化碳減量的策略與方法 12
2-2-1二氧化碳管前處理 12
2-2-2二氧化碳管末控制技術 13
2-2-3 後續處置與再利用 17
2-3 利用氨水或氨氣吸收二氧化碳之反應 20
2-3-1反應機制 20
2-3-2 反應之再利用 22
第三章 研究方法 25
3-1 實驗設備 25
3-1-1 進料系統 25
3-1-2 反應器 28
3-1-3 取樣系統 31
3-1-4 分析儀器 33
3-1-5 再生實驗設備 38
3-1-6 其他實驗設備 39
3-2 實驗材料 41
3-3 實驗方法與步驟 42
3-3-1 實驗規劃 42
3-3-2 實驗前之預備工作 44
3-3-3 實驗步驟 47
第四章 結果與討論 50
4-1 預備實驗 50
4-2 吸收飽和實驗 60
4-2-1 氨水濃度對氨水再生效率的影響 66
4-2-2 不同的氫氧化鈣加藥量對氨水再生效率的影響 71
4-2-3 再生攪拌時間反應動力之探討 74
4-2-4 吸收-再生循環對吸收容量之影響 81
4-2-5 以不同的再生試劑再生氨水之比較 84
4-2-6 IC液相離子分析 88
4-3 輔助實驗 94
第五章 結論與建議 97
5-1 結論 97
5-2 建議 99
參考文獻 101
附錄一 106
附錄二 113
附錄三 115
附錄四 121
附錄五 125
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系統識別號 U0026-0812200910400454
論文名稱(中文) 未飽和層一維入滲解析之研究
論文名稱(英文) The Study of One-Dimensional Infiltration in Unsaturated Soils
校院名稱 成功大學
系所名稱(中) 資源工程學系碩博士班
系所名稱(英) Department of Resources Engineering
學年度 91
學期 2
出版年 92
研究生(中文) 楊岳達
學號 N4690105
學位類別 碩士
語文別 中文
口試日期 2003-05-28
論文頁數 88頁
口試委員 指導教授-李振誥
口試委員-溫志超
口試委員-徐國錦
口試委員-譚義績
關鍵字(中) 數值模式
解析模式
入滲
未飽和層
關鍵字(英) infiltration
numerical solution
unsaturated zone
analytical solution
學科別分類
中文摘要 本研究主要目的在比較Srivastava and Yeh、Babajimopoulos及陳建謀之三種未飽和層一維入滲理論模式之適用性,同時選定一試驗場址,收集其現地相關資料,並且進行現地及室內試驗以求取理論模式所需之土壤特性參數,代入適合理論模式中,配合敏感度分析結果及現場毛細壓力觀測資料進行現場參數模式率定,以作為往後預測試驗場址土壤入滲行為及相關研究之依據。
於試驗場址進行現地及室內試驗,並長期觀測地下水位、土壤毛細壓力及體積含水量變化,搭配日降雨量資料,以求取理論模式所需參數,其中包括飽和透水係數、土壤孔隙分佈參數、地下水位深度、初始入滲率、時間大於零之入滲率、飽和含水量、殘餘含水量。將所得之參數進行敏感度分析,發現飽和透水係數、土壤孔隙分佈參數、地下水位深度、初始入滲率、時間大於零之入滲率對理論模式影響最為顯著,飽和含水量及殘餘含水量對理論模式影響較小。
模式比較部分,Srivastava and Yeh所推導之解析模式及Babajimopoulos所提出之數值模式,其模擬之初始土壤剖面可依現場土壤狀況進行調整,而陳建謀所提出之解析模式,其模擬之初始土壤剖面為地表為殘餘含水量時所形成之穩態剖面,其理論模式模擬之初始土壤剖面無法依照現場土壤狀況調整,因此若模擬事件之入滲率為定值且未產生積水時,Babajimopoulos之理論模式適用性最廣,Srivastava and Yeh之理論模式次之,陳建謀之理論模式較差。而三種理論模式中,僅陳建謀之理論模式考慮積水狀況且其入滲率可為時間之函數,因此若模擬事件發生積水現象或其入滲率為時間函數時,則僅陳建謀之理論模式適用。
利用Srivastava and Yeh及Babajimopoulos之理論模式配合現場毛細壓力觀測值進行現場參數模式之率定,率定所得參數為飽和透水係數為0.30 、土壤孔隙分佈參數為0.0032 、飽和含水量為0.4、殘餘含水量為0.138,將此參數進行預測驗證,發現在模擬出現場之初始土壤剖面後,兩種理論模式之模擬結果其趨勢與現地觀測資料之趨勢符合。
英文摘要 Three models of one-dimensional transient infiltration, the Srivastava and Yeh model, the Babajimoupos model, and Chen model, were provided to analyze in-situ hydraulic data of unsaturated soils in NCKU-RE study site in order to find the most appropriate model and calibrate the effective parameters. Several hydraulic parameters of soil grain size, saturated hydraulic conductivity, soil pore-size distribution parameter, related volumetric water content, daily rainfall data, water-table altitude, saturated water content, and residual water content were investigated in field observation and lab experiment. In the case of field observations systems, soil pressure head and soil volumetric water content were set up to collect the series data in different depths from ground surface in study site. Then we do sensitivity analysis of the parameters of model. The results of sensitivity analysis showed that the effect of saturated hydraulic conductivity, soil pore-size distribution parameter, water-table altitude, initial flux, and prescribed flux for time which is greater than zero were more significant while saturated and residual water contents were less significant.
As compared sensitivity results of three models, it indicated that the Srivastava and Yeh model and Babajimpoulos model are comparable under the similarly initial in-situ soil condition. In the case that infiltration rate is constant and without ponding, the Babajimpoulos model is the most predictive model. But in the case that infiltration rate is a function of time or the ponding is occurred, Chen model is the most appropriate model.
In NCKU-RE study site, we used Srivastava and Yeh model and Babajimpoulos model to calibrate field data, results indicated that the value of saturated hydraulic conductivity is 0.30 , soil pore-size distribution parameter is 0.0032 , saturated water content is 0.4 and residual water content is 0.138. Meanwhile, these two models can use to simulate the trend of pressure head in terms of depth in site observations.

論文目次 目錄

摘要 I
誌謝 V
目錄 VI
表目錄 VIII
圖目錄 IX
符號表 XI

第一章 緒論 1
1.1 研究動機及目的 1
1.2 前人研究 1
1.3 研究方法及流程 5

第二章 理論模式 8
2.1 理論模式(一) 8
2.2 理論模式(二) 12
2.2.1 降雨完全入滲之解 13
2.2.2 降雨部分入滲之解 16
2.3 理論模式(三) 18

第三章 實驗場址之參數推求 22
3.1 實驗場址概述 22
3.2 土壤特性分析 24
3.3 土壤之飽和透水係數 27
3.3.1 定水頭試驗 27
3.3.2 雙環實驗 29
3.4 飽和含水量、殘餘含水量及土壤孔隙分佈參數 31
3.4.1 抽真空飽和皿 31
3.4.2 壓力鍋實驗 32
3.4.3 電子式張力計之率定 35
3.4.4 TDR之設定 39

第四章 模式比較及模式之敏感度分析 43
4.1 理論模式之比較 43
4.1.1 理論模式(一)與理論模式(三)之比較 43
4.1.2 理論模式(一)、理論模式(二)及理論模式(三)之比較 45
4.2 敏感度分析 47
4.2.1 地下水位深度 49
4.2.2 飽和透水係數 52
4.2.3 土壤孔隙分佈參數 54
4.2.4 初始入滲率 56
4.2.5 時間大於零之入滲率 58
4.2.6 飽和含水量 61
4.2.7 殘餘含水量 63

第五章 案例分析 65
5.1 前言 65
5.2 實驗場址長期觀測資料結果 65
5.2.1 地下水位深度長期觀測結果 65
5.2.2 土壤剖面毛細壓力長期觀測結果 66
5.2.3 土壤剖面體積含水量長期觀測結果 67
5.3 現場參數模式率定及驗證 68
5.3.1 模式率定 69
5.3.2 模式驗證(一) 73
5.3.3 模式驗證(二) 76
5.4 討論 79

第六章 結論與建議 81
6.1 結論 81
6.2 建議 82




參考文獻 83
自述 88
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21.Simunek, J., M. Sejna, M. Th. Van Genuchten, “The HYDRUS-2D software package for simulating the two-dimensional movement of water,heat,andmultiple solutes in variably-saturated media.version 2.0”, IGWMCTPS-53.International Ground Water Modeling Center, Colorado School of Mines, Golden, Colorado, pp.251, 1999.
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------------------------------------------------------------------------ 第 3 筆 ---------------------------------------------------------------------
系統識別號 U0026-0812200910414424
論文名稱(中文) 統計分水嶺影像分割之研究
論文名稱(英文) A Study on Statistical Watershed Segmentation
校院名稱 成功大學
系所名稱(中) 統計學系碩博士班
系所名稱(英) Department of Statistics
學年度 91
學期 2
出版年 92
研究生(中文) 王怡穎
學號 r2690406
學位類別 碩士
語文別 英文
口試日期 2003-06-06
論文頁數 68頁
口試委員 指導教授-孫永年
指導教授-吳鐵肩
口試委員-李隆安
關鍵字(中) 分水嶺分割
馬可夫模型
多維解析度
合併
關鍵字(英) Multi-resolution
Markov random field
Merge
Watershed segmentation
學科別分類
中文摘要 在這篇文章中,我們研究的主題集中在影像分析上。影像分割是影像分析的一個重要的過程。我們結合了分水嶺分割法和統計方法找出所感興趣的區域。用分水嶺分割法,有一個缺點就是所分割出來的區域過多,所以我們的目標是用統計方法把過多的區域合併,以達到更好的視覺效果。
首先,為了去掉雜訊,我們用中位數過濾法,使原始影像更平滑。然後由這張影像算出梯度影像,接著用這張梯度影像當成要進行分水嶺分割的影像。正如所預期的,用分水嶺分割出來的影像呈現一區區的小區域,於是我們就用統計方法來解決這個問題。
不同於傳統的方法,我們應用統計方法來處理合併的問題。在合併的第一個步驟,利用多維解析度的概念,找出有信心的邊,作為合併前的前處理步驟。之後,用三種標準逐一地判斷是否要合併小區域,這些標準是經由計算區域的特性而來的。其中,我們有考慮區域間平均數的差異、區域間的變異是否相同、用馬可夫模型來檢測邊有沒有存在的必要。
在這篇文章中,背景介紹在第一章;分水嶺分割法的介紹在2.1節;多維解析度的概念在2.2節;相關的統計方法在第三章;第四章介紹整個合併的流程;實驗結果在第五章;一些討論和結論在第六章。
英文摘要 In this thesis, the area of research is focused on computer image analysis. Image segmentation is one of the most important processes in computer image analysis. In this research, we proposed a hybrid image segmentation method consisting of the watershed technique and a statistic merging process to detect areas of interest from a given image. As this initial segmentation by watershed approach usually suffers from an over-segmented image, our aim is to design the statistic merging mechanism and improve the segmentation results.
At first, in order to reduce the noise, we apply the median filter on the input image. Then, we acquire the gradient image from the resulting image as the new input image, which is then used to implement the watershed transform. The watershed transform obtains an over-segmented result as expected. Therefore, we have to utilize the proposed merge procedure.
Different from the conventional methods, we want to use the statistical method to deal with the merge problem. The merge method employs a multi-resolution procedure together with three merging criteria. A simple multi-resolution edge detection technique is designed as the preprocessing step. Three merging criteria based on the region properties are defined. These criteria are calculated for every small region of the watershed result. Then, we can check the similarity of the adjacent regions between every pair of neighboring regions. By using the Markov random field (MRF) model, we obtain the optimal segmentation by merging regions based on the selected criteria.
In this thesis, background introduction is given in Chapter 1. The watershed transform is described in Section 2.1. The concept of multi-resolution is depicted in Section 2.2. The related statistical methods are presented in Chapter 3. Chapter 4 illustrates the merging procedure. Experimental studies are demonstrated in Chapter 5 with five standard images. And some discussions and conclusions are given in Chapter 6.
論文目次 Chapter 1 Introduction………………..……………….…….1
1.1 Introduction……………………………………...…..1
1.2 Threshold techniques…….……………………..…...3
1.3 Edge-based methods………….…….…...…..……....5
1.4 Region-based methods……….………..………….…6
1.5 Hybrid-methods…………..………....………….…...7
Chapter 2 Watershed algorithm………………………….…9
2.1 Watershed Algorithm…………………………….….9
2.2 Multi-Resolution…………………………..………...…..18
Chapter 3 Statistical methods………….………………..…23
3.1 Bayesian approach………………..…………..……23
3.2 Bartlett test……………….….……………….…….21
3.3 Markov random field………….……………..…….25
3.4 Normal distribution transform………………....…..32
Chapter 4 Merging procedure………………………….…….44
Chapter 5 Experiment results…………………………….…..51
Chapter 6 Discussions and Conclusions………………….….63
Reference………………………………………………………64
Appendix…….…………………………………………...…….67
參考文獻 [1] R. Haralick and L. Shapiro, ‘Image segmentation techniques,’ CVGIP, Vol. 29, pp.100-132 (1985).
[2] K. Mardia and T. Hainsworth, ‘A spatial thresholding method for image
segmentation,’ IEEE Trans. Pattern Anal. Machine Intell., Vol. 10, pp.
919-927, Nov. (1988).
[3] http://sern.ucalgary.ca/courses/cpsc/533/w02/Perception/Perception.ppt
[4] A. Jain, Fundamentals of Digital Image Processing. Englewood Cliffs, NJ; Prentice-Hall, (1989).
[5] V. Nalwa, A Guided Tour of Computer Vision. Reading, MA: Addison-
Wesley, (1993).
[6] J. Canny, ‘A computational approach to edge detection,’ IEEE Trans.
Pattern Anal. Machine. Intell., Vol. PAMI-8, pp. 679-698, Nov. (1986).
[7] D. Marr and E. Hildreth, ‘Theory of edge detection,’ in Proc. R. Soc. Lond. B, no. 207, pp. 187-217. (1980).
[8] S. Horowitz and T. Pavlidis, “Picture segmentation by a tree traversal
algorithm,” J. Assoc. Comput. Mach., Vol. 23, pp. 368-388, Apr. (1976).
[9] S. Chen, W. Lin, and C. Chen, ‘Split-and-merge image segmentation
based on localized feature analysis and statistical tests,’ CVGIP: Graph.
Models Image Process., Vol. 53, pp. 457-475, Sept. (1991).
[10] P. Besl and R. Jain, ‘Segmentation through variable-order surface
fitting,’ IEEE Trans. Pattern Anal. Machine Intell., Vol. 10, pp. 167-192,
Mar. (1988).
[11] T. Pavlidis and Y. Liow, ‘Integrating region growing and edge detection,’
IEEE Trans. Pattern Anal. Machine Intell., Vol. 12, pp. 225-233,
Mar. (1990).
[12] L. D. Griffin, A. C. F. Colchester, and G. P. Robinson, ‘Scale and
segmentation of grey-level images using maximum gradient paths,’
Image Vis. Comput., Vol. 10, pp. 389-402, July/Aug. (1992).

[13] F. Meyer and S. Beucher, ‘Morphological segmentation,’ J. Vis. Commun.
Image Represent., Vol. 1, pp. 21-46, Sept. (1990).
[14] J. M. Gauch and S. M. Pizer, ‘Multiresolution analysis of ridges and
valleys in gray-scale images,’ IEEE Trans. Pattern Anal. Machine Intell.,
Vol. 15, pp. 635-646, June (1993).
[15] Kostas Haris, Serafim N. Efstratiadis, Member, IEEE, Nicos Maglaveras, Member, IEEE,and Aggelos K. Katsaggelos, Fellow, IEEE, ‘Hybrid Image Segmentation Using Watersheds and Fast Region Merging,’ IEEE Transactions On Image Processing, Vol. 7, no. 12, December (1998).
[16] L. Vincent and P. Soille, ‘Watersheds in digital spaces: An efficient
algorithm based on immersion simulations,’ IEEE Trans. Pattern Anal.
Machine Intell., Vol. 13, pp. 583-598, June (1991).
[17] C.-T. Li, ‘Multiresolution image segmentation integrating Gibbs sampler
and region merging algorithm,’ Signal Processing, 83, pp.67-78 (2003).
[18] Jong-Bae Kim, Hang-Joon Kim, ‘Multiresolution-based watersheds for efficient
image segmentation,’ Pattern Recognition Letters, 24, pp.473-488 (2003).
[19] Richard R. Schultz, Student Member, IEEE, and Robert L. Stevenson, Member, IEEE, ‘A Bayesian Approach to Image Expansion for Improved Definition,’ IEEE Transactions On Image Processing, Vol. 3, no. 3, May (1994).
[20] James O. Berger. Statistical Decision Theory and Bayesian Analysis (1985).
[21] Bartlett, M. S., Properties of sufficiency and statistical tests. Proceedings of the Royal Statistical Society Series A 160, pp. 268–282 (1937).
[22] Stan Z. Li. Markov Random Field Modeling in Image Analysis. Computer Science Workbench Series Editor: Tosiyasu L. Kunii (2001).
[23] Geman, S. and Geman, D. ‘Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images,’ IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6): pp. 721-741 (1984).
[24] Derin, H. and Elliott, H. ‘Modeling and segmentation of noisy and textured images using Gibbs random fields,’ IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(1): pp. 39-55 (1987).
[25] Geiger, D. and Girosi, F, ‘Parallel and deterministic algorithms from MRF's: surface reconstruction,’ IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(5): pp.401-412. (1991)
[26] Rangarajan, A. and Chellappa, R., ‘Generalized graduated non-convexity algorithm for maximum a posteriori image estimation,’ In Proceedings of International Conference Pattern Recognition, pp. 127-133 (1990).
[27] Shapiro, S. S. and Wilk, M. B., ‘An analysis of variance test for normality (complete samples)’, Biometrika, 52, 3 and 4, pp. 591-611 (1965).
[28] Box, G.E.P. and D.R. Cox, An analysis of transformations (with discussion). J. Royal Statist. Soc. Ser. B, 26: pp. 211-246 (1964).
[29] Anjan Sarkar, Manoj K. Biswas, and K. M. S. Sharma, ‘A Simple Unsupervised MRF Model Based Image Segmentation Approach’ IEEE Transactions On Image Processing, Vol. 9, no. 5, May (2000).
[30] Michael W. Hansen and William E. Higgins, ‘Watershed-Based Maximum-Homogeneity Filtering,’ IEEE Transactions On Image Processing, Vol. 8, no. 7, July (1999).

------------------------------------------------------------------------ 第 4 筆 ---------------------------------------------------------------------
系統識別號 U0026-0812200910435281
論文名稱(中文) 超解析技術應用於影像序列放大之研究
論文名稱(英文) Super-resolution Techniques for Image Sequence Enlargement
校院名稱 成功大學
系所名稱(中) 資訊工程學系碩博士班
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 91
學期 2
出版年 92
研究生(中文) 黃一民
學號 p7690131
學位類別 碩士
語文別 英文
口試日期 2003-07-15
論文頁數 80頁
口試委員 指導教授-孫永年
口試委員-簡仁宗
口試委員-蔣榮先
口試委員-蘇文鈺
口試委員-陳永昌
口試委員-柯建全
口試委員-盧天麒
關鍵字(中) 影像序列放大
超解析度
關鍵字(英) super-resolution
image sequence enlargement
學科別分類
中文摘要 由於受限於硬體技術,影像放大(image enlargement)通常經由軟體來完成,並且在影像處理中成為一項重要的研究課題。傳統上,影像內插(image interpolation)只能從單一影像來完成影像放大,影像品質因此受到很大的限制。超解析度放大(super-resolution enlargement)演算法則可將多張影像視為額外的資訊,來估算出一張高解析度影像。假設有足夠的觀察低解析度影像序列(observed low-resolution image sequence),且在影像序列中包含有次像素移動(subpixel shifts),那麼高解析影像就可以估算出來。在影像序列放大中會遇到兩個主要的問題,一個是移動估計(motion estimation),另一個是超解析度演算法,用以將觀察影像序列及移動估計的結果當作輸入,產生一張高解析度影像。此法利用影像序列中之空間與時間資訊以製造出更高解析度的影像。
在本篇論文中,我們根據超解析度放大演算法來提出一個完善的系統,以處理影像序列放大的問題。這個系統主要包含三大步驟,第一個是改進的階層式區塊比對(hierarchical block matching),此方法包含部分扭曲排除(partial distortion elimination),第二個是改進的多層次放大,此方法是根據疊代式解析度加強(iterative resolution enhancement)演算法所改進的,第三個是強健式點對應(robust point matching)方法,此方法是用影像序列的中央影像與靜態影像來做影像對位。在本研究中,我們提出的多層次放大方法所產生的結果,比傳統方法更能夠有效加強影像序列之解析度,視覺上的以及量測數字上的評估均顯示顯著的改進,我們提出的系統在計算上也更為快速。藉由強健式點對應方法,我們也成功地將靜態影像合併到影像序列,並且得到更好的放大影像序列。
英文摘要 Due to hardware limitation, image enlargement (magnification) is mostly carried out by software and becomes an important research task in image processing. Traditionally, image interpolation can only be done with a single image frame. Thus, its quality is greatly constrained. Super-resolution enlargement algorithm is proposed that uses multiple frames as additional information to estimate the high-resolution image. Given enough observed low-resolution image frames with sub-pixel shifts, the construction of high-resolution image can be computed. Two major problems in super-resolution enlargement are the motion estimation and the super-resolution algorithm, which takes the observed frames and the estimated motion as the inputs and produces a high-resolution image. In other words, it utilizes both the spatial and temporal information in an image sequence in creating a higher resolution image.

In this thesis, a complete system based on super-resolution enlargement algorithm is proposed for image sequence enlargement. This system consists of three major steps, the modified hierarchical block matching method together with partial distortion elimination, the modified multilevel magnification based on an iterative resolution enhancement algorithm, and the robust point matching method used to register the center image frame and the still image. In this study, the proposed multilevel magnification method achieves better magnification quality than the traditional one. Besides, both the visual and quantitative improvements are significant, the proposed system is also faster in computation. The image registration by the robust point matching algorithm is also successful in combing a still high-resolution image to the given image sequence and obtains even better magnification results.
論文目次 Chinese Abstract ....................................................... Ⅰ
English Abstract ....................................................... Ⅲ
Contents ............................................................ Ⅴ
List of Figures ........................................................ Ⅶ
List of Tables .......................................................... Ⅸ
Chapter 1 Introduction ................................................. 1
1.1 Motivation ...................................................... 1
1.2 Previous work .................................................... 3
1.3 Outlines ....................................................... 6
Chapter 2 Image sequence enlargement .................................... 8
2.1 Problem formulation and model description ............................. 8
2.2 Traditional bilinear interpolation .................................... 12
2.3 Traditional image sequence enlargement ............................. 14
2.3.1 Motion estimation ......................................... 14
2.3.1.1 Block matching algorithm .............................. 15
2.3.1.2 Hierarchical block matching algorithm ................... 17
2.3.1.3 Elimination of inaccurate motion vector .................... 18
2.3.2 Super-resolution algorithm .................................. 20
2.3.2.1 Iterative algorithm .................................... 20
2.3.2.2 Related algorithm for image sequence enlargement ........... 25
2.3.2.2.1 Iterated back projection method ................... 25
2.3.2.2.2 Bayesian MAP method ........................... 27
2.3.2.3 Comparison between different algorithms ................. 29
2.4 Modifications of image sequence enlargement .......................... 30
2.4.1 Super-resolution algorithm ................................... 31
2.4.2 Motion estimation ....................................... 35
Chapter 3 Combining video with still image and applications ................ 38
3.1 Problem description ............................................... 38
3.2 Feature extraction ............................................... 40
3.3 Robust point matching method (RPM method) ......................... 42
3.4 Color adjustment method ......................................... 48
3.5 How to combine video with still image ............................... 48
Chapter 4 Experimental results and discussion ............................. 51
4.1 Experimental environment ....................................... 51
4.2 Experimental results .............................................. 51
4.2.1 Results of super-resolution image ........................... 53
4.2.2 Results of super-resolution image sequence combined with still
......................................................... 63
4.3 Discussion .................................................... 72
Chapter 5 Conclusion and future work .................................... 75
5.1 Conclusion .................................................... 75
5.2 Future work ..................................................... 76
References ......................................................... 77
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