Image Motion Deblurring using Bi-level Regions｜國立清華大學博碩士論文庫

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研究生：	林育模 Yu-Mo Lin
論文名稱：	Image Motion Deblurring using Bi-level Regions 利用二階區塊還原動態模糊影像
指導教授：	賴尚宏 Shang-Hong Lai
口試委員:
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 資訊工程學系 Computer Science
論文出版年：	2008
畢業學年度：	96
語文別：	英文
論文頁數：	48
中文關鍵詞：	影像還原、動態模糊、二階區塊
外文關鍵詞：	Deblur, Image, Bi-level, Restoration, Regions
相關次數：	點閱：1 下載：0
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在這篇論文中，我們提出了從單張模糊影像中利用二階區塊的影像還原演算法，我們的方法可以還原運動模糊之數位影像並且此模糊點擴散函數不需具有任何特定的參數型式。首先，我們先手動選取多個二階區塊，希望藉由二階區塊的幫助來估算出點擴散函數。接著我們提出一個搜尋演算法來尋找最好的初始估計，此方法包含藉由我們定義的能量函數來尋找最好的門檻值以及利用多項式光罩模型處理些微光影變化所照成的影響。我們在此論文中提出一個機率模型將動態模糊估計及影像還原結合成ㄧ個統計估測的形式。針對此模型，我們利用了交替演算法反覆地調整動點擴散函數來得到更好的影像還原結果。在估計出動態模糊之後，我們再利用Richardson-Lucy影像還原演算法來對整張影像進行影像還原。經由實驗結果的呈現，包含對模擬模糊影像及真實模糊影像的影像還原結果，我們驗證了本篇論文所提出之動態模糊影像還原演算法的功效。

In this thesis, we propose a novel image restoration framework for restoring images degraded by unknown motion blurs from a single image. Our approach takes advantage of the bi-level image patches to estimate the blur kernel. The patches which contain only two-grayscale regions are first selected. Then we propose a method to find an initial guess for our algorithm. This method includes searching a threshold value based on a cost function and utilizing an illumination model to account for small illumination variations. Afterwards, we propose a probabilistic model which combines both blur kernel estimation and non-blind bi-level image deconvolution into a single maximum a posteriori (MAP) formulation. An alternating minimization algorithm is developed to iteratively refine both the blur kernel and the bi-level blurred patches. Finally, we apply Richardson-Lucy (RL) deconvolution to restore the entire image by using the estimated blur kernel. Some experimental results on the deblurring of simulated and real blurred images are given to demonstrate the performance of the proposed blind motion deblurring algorithm.

Contents

Chapter 1 Introduction    1
1.1 Problem Background    1
1.2 Problem Description    2
1.3 Previous Works    3
1.4 Main Contribution    8
1.5 Thesis Organization    9
Chapter 2 Proposed Motion Deblurring Algorithm using Bi-level Image Patches    10
2.1 System Overview    10
2.2 Blur Kernel Estimation using Bi-level patches    12
2.2.1 Definition of the Likelihood Term    13
2.2.2 Definition of the Prior on the Ideal Unblurred Image    14
2.2.3 Definition of the Prior on the Blur Kernel    19
2.3 Energy Minimization    21
2.3.1 Optimization for Latent image F    22
2.3.2 Optimization for Blur Kernel H    24
2.4 Initial Image Patch Thresholding    25
2.4.1 Image Patch Adjustment    26
2.4.2 Image Patch Thresholding    26
2.4.3 Image Patch Illumination Modeling    28
Chapter 3 Experimental Results    31
3.1 Experiments on Simulated Blurred Images    31
3.2 Experiments on Real Blurred Images    38
Chapter 4 Discussion and Conclusion    43
References    46





List of Figures

Fig. 1. Flowchart of the proposed bi-level-based blind motion deblurring algorithm.    12
Fig. 2. Overview of our proposed blur kernel estimation algorithm.    12
Fig. 3. Ill-posed problem for blind image deconvolution.    15
Fig. 4. The distribution of image gradients.    17
Fig. 5. Effect of the sparse prior Ps(F).    18
Fig. 6. Effect of the locally smooth prior Pm(F).    19
Fig. 7. The heavy-tailed curve of kernel values.    21
Fig. 8. Flowchart of our proposed image patch thresholding algorithm.    26
Fig. 9. The effect of the illumination model.    29
Fig. 10. Poster photo deblurring experiment.    34
Fig. 11. Map photo deblurring experiment.    35
Fig. 12. Results of different iterations in our deconvolution algorithm.    36
Fig. 13. Stadium photo deblurring experiment.    37
Fig. 14. Garage photo deblurring experiment.    37
Fig. 15. Deblurring on a real blurred stadium photo.    40
Fig. 16. A deblurring experiment on a real blurred image.    41
Fig. 17. A real blurred image.    42
Fig. 18. A failed deblurring example.    45

List of Tables

Table 1. Comparison of the restoration results.    38

                                

References

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