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研究生：	陳昱宏 Yuh-Horng Chen
論文名稱：	低複雜度多尺度梯度色彩插補演算法 Low-complexity Color Demosaicing Based on Multiscale Gradients
指導教授：	蘇崇彥 Su, Chung-Yen
學位類別：	碩士 Master
系所名稱：	電機工程學系 Department of Electrical Engineering
論文出版年：	2013
畢業學年度：	102
語文別：	中文
論文頁數：	47
中文關鍵詞：	解馬賽克、色彩插補、多尺度、低複雜度
英文關鍵詞：	Demosaicing, color interpolation, multiscale, low-complexity
論文種類：	學術論文
相關次數：	點閱：110 下載：3
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數位相機色彩插補是現在單一感測器數位相機之數位影像處理流程中最重要的部
份之一。近年來有許多基於分類器的色彩插補演算法被提出,傳統分類器色彩插補演
算法雖然再大多數的場合表現的極為優秀,但是存在著因分類錯誤造成嚴重錯色的問
題,因此我們試圖尋找不一樣的解決方案。近年來多尺度梯度演算法 (MSG)是最成功
的演算法之一,其擁有相當卓越的影像品質。多尺度梯度演算法使用權重混合的方式
來取代傳統分類器的作法,雖然這樣的作法可以大幅提高影像品質,但是相對的也大
大的提高計算複雜度。在本篇論文中我們仔細對多尺度梯度演算法進行了分析,研究
其權重設計,試圖尋找一種適合的替代設計,能在維持多尺度梯度演算法的影像品質
的情況下,降低其計算複雜度。本方法主要特點在於,合併了兩階段綠色平面插補的
權重設計。在初始插補綠色平面階段以及優化綠色平面階段能使用同一組權重矩陣設
計,有效的降低計算複雜度。實驗結果證明我們不僅在 PSNR 能取得與多尺度演算法
接近的表現,在較為接近人類視覺的 S-CIELab 也能有接近多尺度梯度演算法的表現。
在執行時間上,比起多尺度梯度演算法有明顯的改進,其方法一執行時間約減少
36.37%,而方法二則減少了 41.52%。

Demosaicing of the color filter array (CFA) is one of the most important parts of the
image processing pipeline for single sensor digital cameras. In the recent years, many
classifer-based demosaicing algorithms have been proposed and achieved excellent
performance in most situations. However, occasionally these algorithms generate results
with color artifacts due to wrong directional leads. Recently, the multiscale gradients (MSG)
algorithm was reported, which has the best reported image quality to this date. The MSG
algorithm used adaptive weight design to replace the conventional classifer-based design.
This design boosts the image quality; however, it made a trade-off with computational
complexity. In this thesis, modification and improvement to the MSG algorithm by low-
complexity multiscale gradients algorithm is attempted such that the computational
complexity is significantly reduced while maintaining the high image quality possessed by
the original MSG algorithm. Experimental results show that the proposed algorithm not
only maintains peak signal-to-noise ratio (PSNR) and S-CIELab. Two methods were
proposed in this thesis and the speeds compared to the original MSG algorithm were
increased by 36.37% and 41.52%, respectively, which is a significant improvement.

摘　要..........................................................................................................................................i
ABSTRACT...............................................................................................................................ii
誌　謝.......................................................................................................................................iii
目　錄........................................................................................................................................iv
表 目 錄.....................................................................................................................................v
圖 目 錄....................................................................................................................................vi
第一章 緒論...............................................................................................................................1
1.1 研究背景...........................................................................................................................1
1.2 研究動機...........................................................................................................................3
1.3 論文架構...........................................................................................................................6
第二章 文獻探討........................................................................................................................8
2.1 可適應色彩平面差補演算法(ACPI).................................................................................8
2.2 高效子頻帶相關性色彩插補法(EDUSC).......................................................................10
2.3 多尺度梯度插補法(MSG)..............................................................................................14
第三章 低複雜度多尺度梯度插補法........................................................................................19
3.1 初始梯度資訊.................................................................................................................19
3.2 插補綠色平面.................................................................................................................22
3.3 更新綠色平面.................................................................................................................23
3.4 插補紅色藍色平面..........................................................................................................24
第四章 實驗數據與模擬結果...................................................................................................26
4.1 實驗流程.........................................................................................................................28
4.2 計算複雜度分析.............................................................................................................28
4.3 PSNR比較.....................................................................................................................31
4.4 S-CIELab比較...............................................................................................................34
4.5 影像視覺效果比較..........................................................................................................36
4.6 McMaster圖集比較.......................................................................................................39
第五章 結論與未來工作...........................................................................................................43
參考文獻...................................................................................................................................44

                                

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2009.
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[8] C.-Y. Su and W.-C. Kao, “Effective demosaicing using subband correlation,” IEEE
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[9] J. F. Hamilton Jr. and J. E. Adams, “Adaptive color plane interpolation in single color
electronic camera,” U. S. Patent 5 629 734, May 1997.
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[11] S-CIELab Metric (2003). [Online]. Available at http://white.stanford.edu/~brian/
scielab/scielab.html.
[12] Kodak test images and the demosaicing code of successive approximation. [Online].
Available at http://www.csee.wvu.edu/~xinl/demo/demosaic.html.
[13] GNU GCC Website. [Online]. http://gcc.gnu.org/.
[14] CMake Website. [Online]. http://www.cmake.org/.
[15] OpenCV Website. [Online]. http://opencv.org/.
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interpolation and non-local adaptive thresholding,” Journal of Electronic Imaging
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[21] L. Zhang, “Mcmaster dataset.” [Online] http://www4.comp.polyu.edu.hk/~cslzhang/CDM_ Dataset.htm.

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