隨著影像多媒體技術的進步發達，隨時隨地且快速拍攝照片已是
勢不可擋的需求，快速地拍照過程難免產生不理想的失焦照片，解決
相片失焦的影像事後對焦技術也隨之產生。計算相機為應用於影像事
後對焦的技術研究之一，然而至今的計算相機由於裝置複雜且造價不
斐不利實現與攜帶，因此我們提出一個新穎、便利可攜式、易於實現
的光柵計算相機，此光柵計算相機拍攝的照片基於繞射光柵的光學原
理會產生影像鬼影的失真現象，依照光柵原理，假設光柵產生的影像
鬼影的位移程度與拍攝物件的深度有關，則從此鬼影失真中萃取出深
度資訊將可應用於影像事後對焦技術。本論文中以此具有鬼影失真的
影像進行: 1) 深度估計、2) 影像還原以及 3) 影像事後對焦研究；假
設影像鬼影可以點擴散函數描述，以非盲反卷積運算得影格之點擴散
函數，運用 k平均演算法將點擴散函數分為與深度相關群組，進而產
生深度圖。另外，我們設計光柵的點擴散函數並以非盲反卷積進行影
像還原。除此之外，以高斯模糊函數模擬真實的對焦模糊效果應用於
影像事後對焦。最後在實驗結果中，證明了光柵計算相機產生的影像
鬼影失真與物體所在深度成反比。

In this paper, we present a novel computational camera, namely, gratingbased computational camera (GCC). The camera utilizes diffraction grating to generate a ghosting image, which results from the diffraction grating physical properties in optics and contains depth information for image refocusing. Since the point spread function (PSF) of the ghosting image is spatial variant, we propose a method of estimating the PSF from the ghosting image captured by GCC. Our method first estimates the PSFs by non-blind deconvolution. We then cluster the PSF map into several
clusters with various depths and compute the variance of PSF in each cluster as depth features. Furthermore, principal component analysis (PCA) is applied before clustering to reduce the dimensionality of the features. Since the distortion on GCC is not avoidable, the third step before
refocusing is to restore the grating-degraded image by designing a PSF for de-ghosting. Our experimental results show the efficacy of the proposed scheme.

摘要...................................................... i
Abstraction ............................................. ii
誌謝 ................................................... iii
Content ................................................. iv
Chapter 1 Introduction ................................... 1
Chapter 2 Related Work ................................... 5
2.1. Image Deconvolution ................................. 5
2.2. Image Refocusing using Computational Camera ......... 7
Chapter 3 Proposed Method ................................ 8
3.1. Grating-based Computational Camera .................. 8
Chapter 4 Proposed Method ............................... 14
4.1. Depth Estimation ................................... 14
4.1.1. Point-Spread Function and Estimation ............. 15
4.1.2. Dimensionality Reduction ......................... 17
4.1.3. Depth Information Extraction ..................... 17
4.2. Image Restoration .................................. 18
4.3. Image Refocusing ................................... 19
Chapter 5 Experimental Results and Discussion ........... 26
5.1. Grating-based Computational camera ................. 26
5.2. Depth Estimation ................................... 27
5.3. Image Refocusing ................................... 28
5.4. Discussion ........................ .................28
Chapter 6 Conclusion..................................... 37
Reference ............................................... 38

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