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研究生: 林啟銘
Chi-Ming Lin
論文名稱: 基於鄰近像素方向資訊之數位相機CFA內插演算法設計
Design of a CFA Demosaicing Based on Directional Information of Neighboring Pixels for Digital Still Cameras
指導教授: 蘇崇彥
Su, Chung-Yen
學位類別: 碩士
Master
系所名稱: 工業教育學系
Department of Industrial Education
論文出版年: 2008
畢業學年度: 96
語文別: 中文
論文頁數: 69
中文關鍵詞: 色彩內插解馬賽克貝爾圖形數位相機
英文關鍵詞: Color interpolation, Demosaicing, Bayer pattern, Digital still camera
論文種類: 學術論文
相關次數: 點閱:175下載:8
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  • 為了減少硬體的成本,目前大部分消費型的數位相機都僅使用單一感光元件覆蓋一層色彩濾波陣列去記錄場景中的顏色。色彩內插演算法則是一種用於單一感光元件數位相機的影像處理程序,藉由內插缺少顏色的像素值來重建出全彩的影像。本論文提出一種低複雜度且有效抑制人工錯色的色彩內插演算法。在第一個步驟中,使用鄰近像素的邊緣方向資訊去判斷缺少綠色像素之內插方向;接著使用色彩差值的內插演算法去估測缺少的紅色與藍色像素。在第二個步驟中,本文提出一種新的優化內插演算法,使用色彩差值空間的中值濾波去更新綠色像素;接著使用一種改良的中值濾波方法去提升紅色與藍色像素的品質。實驗結果顯示,本論文所提出來的色彩內插演算法,在主觀的視覺品質與客觀的峰值訊號雜訊比與S-CIELab的評估上,都比近期內所提出來的方法有明顯的改善與提升。

    In order to reduce the hardware cost, most digital still cameras(DSCs) use only single-sensor equipped with a color filter array(CFA) to capture the color of the scene presently. Demosaicing algorithm is a process of estimating the missing color values for full-images from incomplete color samples acquired by single-sensor digital still cameras. This paper presents a low-complexity and effective demosaicing algorithm to suppress the color artifacts. In the first step, the directional information of neighboring pixels is used to determine the interpolated directions of missing green pixels; a color-difference algorithm is next used to handle red and blue ones. In the second step, a new refinement algorithm is proposed to update green pixels by a median filter in color-difference space and a modified median filter to refine the other two. Experimental results show that the proposed method is better than the state-of-the-art methods on visually quality subjectively and peak signal-to-noise ratio and S-CIELab metric objectively.

    目錄 謝誌 i 摘要 ii Abstract iii 目錄 iv 圖目錄 vi 表目錄 viii 第一章 緒論 1 1.1. 研究背景 1 1.2. 研究動機 5 1.3. 本文架構 7 第二章 相關文獻探討 9 2.1. 非適應性內插演算法 9 2.1.1. 雙線性內插演算法 10 2.1.2. 固定色調內插演算法 12 2.1.3. 有效的訊號相關內插演算法 14 2.2. 適應型內插演算法 17 2.2.1. 邊緣偵測色彩內插演算法 17 2.2.2. 可適應性色彩平面內插演算法 19 2.2.3. 有效空間關聯內插演算法 21 2.2.4. 中值濾波內插演算法 23 2.3. 疊代型內插演算法 25 2.3.1. 連續逼近內插演算法 25 2.3.2. 有效加權邊緣與色彩相關內插演算法 28 第三章 基於鄰近像素方向資訊之內插演算法 33 3.1. 基於鄰近像素方向資訊內插演算法之整體架構 35 3.2. 初始演算法處理流程說明 36 3.2.1. 初始階段綠色像素的估測方法 36 3.2.2. 初始階段紅色與藍色像素的估測方法 40 3.3. 優化演算法處理流程說明 41 3.4. 鄰近像素方向資訊之權重值與門檻值分析 44 第四章 實驗流程與模擬結果比較 47 4.1. 實驗模擬流程簡介 48 4.2. PSNR值比較 51 4.3. S-CIELab值比較 56 4.4. 重建影像視覺比較 58 4.5. 計算複雜度分析 64 第五章 結論與未來工作 66 參考文獻 67 圖目錄 圖1-1 三個感光元件(Three-CDD)之數位相機架構 3 圖1-2 單一個感光元件(Single-CCD)之數位相機架構 4 圖1-3 貝爾圖形 5 圖1-4 假色現象(False color) 6 圖1-5 拉鏈型效應(Zipper effect) 6 圖2-1 雙線性內插演算法參考圖形 10 圖2-2 原圖與雙線性內插演算法重建影像之比較 12 圖2-3 固定色調內插演算法參考圖形 13 圖2-4 有效的訊號相關內插演算法參考圖形 15 圖2-5 邊緣偵測色彩內插演算法參考圖形 18 圖2-6 可適應性色彩平面內插演算法參考圖形 20 圖2-7 有效加權邊緣與色彩相關內插演算法流程圖 29 圖2-8 有效加權邊緣與色彩相關內插演算法參考圖形 30 圖3-1 基於鄰近像素方向資訊內插演算法之流程圖 35 圖3-2 本研究內插演算法參考貝爾圖形 37 圖3-3 Hamiltom邊緣偵測可能誤判情形 38 圖3-4 邊緣方向修正機制參考圖形 38 圖3-5 優化內插演算法參考圖形 43 圖3-6 Image8使用ACPI方法判斷式選擇到的估測值情形 45 圖3-7 24張測試影像初始內插G平面的平均PSNR值 46 圖3-8 Image8使用本研究所提出來的方法選擇到的估測值情形 46 圖4-1 實驗模擬流程圖 48 圖4-2 本研究所使用24張柯達公司測試影像[23] 50 圖4-3 24張測試影像RGB平面之平均PSNR值長條圖 55 圖4-4 Image8局部放大影像與各種演算法之重建影像 60 圖4-5 Image5局部放大影像與各種演算法之重建影像 63 表目錄 表3-1 典型優化演算法與本文優化演算法計算複雜度比較 43 表4-1 每一張測試影像峰值訊號雜訊比(PSNR)(單位:dB) 53 表4-2 24張測試影像RGB平面之平均PSNR值(單位:dB) 55 表4-3 每一張測試影像S-CIELab值 57 表4-4 本文所提出來基於鄰近素方向資訊內插演算法之計算複雜度 65

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