Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/9058
標題: 一種基於YUV色度空間的解馬賽克方法
Novel Demosaicking method based on the YUV space
作者: 蔡政嶔
Tsai, Chung-Chin
關鍵字: 色彩濾波陣列;CFA;像感測器;解馬賽克;貝爾色彩濾波陣列;image sensor;image sensor;Bayer CFA
出版社: 電機工程學系所
引用: [1] B. E. Bayer ,”Color imaging array, ”U.S. Patent No.3971065,July 1976. [2] J. Adams, K. Parsulski, and K. Spaulding, “Color processing in digital cameras,” IEEE Micro, pp. 20–29, Nov.–Dec. 1998. [3] W. B. Pennebaker and J. L. Mitchell, JPEG Still Image Data Compression Standard. New York: Van Nostrand Reinhold, 1993. [4] R. Lukac and K. N. Plataniotis, “Digital camera zooming on color filter array,” IEEE Electronics Letters vol. 39, No. 2, Dec. 2003. [5] Rastislav Lukac, Konstantinos N. Plataniotis and Dimitrios Hatzinakos, “Color Image Zooming on the Bayer Pattern,” IEEE Trans. Circuit and Systems Video Technol, vol. 15, No. 11, Nov. 2005. [6] K. Hirakawa and T. W. Parks, “Joint demosaicking and denoising,” IEEE Trans. Image Process, vol. 15, No. 8, pp. 2146-2157, Aug. 2006. [7] Lei Zhang, Xiaolin Wu and David Zhang, “Color Reproduction From Noisy CFA Data of Single Sensor Digital Cameras,” IEEE Trans. Image Processing, vol. 16, No. 9, Sep. 2007. [8] 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. [9] R. Rajeev, E. Wesley, L. Griff, and S. William, “Demosaicking method for bayer color array,” Journal of Electronic Image, 2002 [10] D. Cok, “Signal processing method and apparatus for producing interpolated chrominance value in a sample color image single,” U.S. Patent 4 642 678, 1987. [11] S.C.pei and I.K.Tam,” Effective Color Interpolation in CCD color filter arrays using signal correlation,” IEEE trans.Circuits and System for Video Technology,vol.13,pp.503-513 june 2003 [12] J. E. Adams Jr., “Interactions between color plane interpolation and other image processing functions in electronic photography,” in Proc. SPIE, vol. 2416, C. Anagnostopoulos and M. Lesser, Eds., Bellingham, WA, 1995, pp. 144–151. [13] Hamilton, J. F. Jr. and J. E. Adams, Adaptive Color Plane Interpolation in Single Sensor Color Electronic Camera, United States Patent, 5629734, 1997. [14] X. L. Wu, N. Zhang, “Primary-consistent soft-decision color demosaicking for digital cameras (patent pending),” IEEE Transaction on Image Processing, vol. 13, no 9, pp. 1263-1274, September 2004. [15] Gunturk, B. K., Y. Altunbasak, and R. M. Mersereau, “Color plane interpolation using alternating projections, ”IEEE Transactions on Image Processing, vol.11, no.9, pp.997-1013, 2002. [16] 廖怡欽, “以向量量化方法改善數位色彩影相品質,” 逢甲大學博士論文, 2003 5. [17] 莊啟新, “使用向量量化方法改善色彩濾片陣列內插影相品質,” 逢甲大學碩士論文, 2003 [18] X. Li, “Demosaicing by successive approximation, ”IEEE Trans. Image Process., vol.14, no.3, pp.370-379, March 2005 [19] C. Y. Su, “Highly effective iterative demosaicing using weighted-edge and color-difference interpolation,” IEEE Trans. Consumer Electronics, vol. 52, no. 2,pp. 639-645, May 2006. [20] J. R. Janesick, “Scientific Charge-Coupled Devices,” Bellingham,WA: SPIE, 2001. [21] Bosco A., Mancuso M., “Adaptive Filtering For Image Denoising,” IEEE Proceedings of ICCE2001, pp.208-209, Los Angeles, June 2001. [22] Bosco A., “Adaptive Image Denoising On Bayer Pattern,” ST Journal of System Research. Vol.2, No.2, Dec.2001. [23] Bosco A., Mancuso M., Battiato S., Spampinato G., “Temporal Noise Reduction of Bayer Matrixed Video Data,” Proceedings of IEEE ICME’02 International Conference on Multimedia and Expo 2002 , pp.681-684 , Lausanne, Switzerland, August 2002. [24] Bosco A.., Bruna A.., Santoro G., Vivirito, P., “Joint Gaussian noise reduction and defects correction in raw digital images,” Signal Processing Symposium, 2004. NORSIG 2004. Proceedings of the 6th Nordic, pp. 109-112, 2004. Hirakawa K., ParksT. W., “Image denoising using total least squares,” IEEE Transactions on Image Processing, vol.15, no.9, pp. 2730-2742, 2006. [25] Sakamoto, C. Nakanishi, and T. Hase, “Software pixel interpolation for digital till cameras suitable for a 32-Bit MCU,” IEEE Trans. Consumer Electron., vol. 44, pp. 1342–1352, Nov. 1998.
摘要: 
目前的數位相機為了降低成本與硬體空間,採用單一影像感測器(image sensor)拍攝影像,常見覆蓋於感測器上的色彩濾波陣列(color filter array, CFA)為貝爾色彩濾波陣列(Bayer CFA)。光經由色彩濾波陣列到每一點像素位置,每一點像素位置上感測器只擷取紅色、綠色或藍色其中一種色彩強度值,而最後顯示的全彩影像(full-color image)則是利用擷取的貝爾色彩影像經由重建處理步驟還原,此重建處理步驟稱為解馬賽克(image sensor)演算法或色彩濾波陣列內插法(CFA interpolation)。
本文提出一種改良primary-consistent soft-decision(PCSD)之高效率演算法,此方法有兩個特點。第一特點是將以往的顏色相差平面(R-G或是B-G)轉換為色度空間平面(YUV空間的UV色度空間)進行軟性判斷(soft-decision)。第二特點是後續處理中非使用常見的中值濾波器(median filter),而是加入錯色減少程序(false-color reduction process),經由錯色減少程序大幅減少因判斷錯誤形成的錯色(false color)區域。另外經由本文實驗結果得知,執行兩次本文提出的方法之輸出全彩圖形視覺上更接近原始圖形與提高影像品質。
本文第一章首先介紹目前常見的解馬賽克演算法,並針對各種演算法探討其優缺點。
第二章提出本研究的演算法。針對軟性判斷無法正確判斷所產生的錯色區域,經由顏色像差平面轉換為色度空間平面與錯色減少程序改善,進一步執行兩次本文提出的方法使輸出全彩圖形更能接近原始影像。
第三章針對現有演算法與本文提出的演算法透過視覺、數值等方式說明視覺上影像輸出結果與影像品質皆有改善與進步。
第四章估測各種演算法的內插還原影像方式所產生的影像誤差,誤差越小則是說明該還原影像的品質越接近原始影像。第五章中針對前面實驗的結果,提出可能改善的方法。

To reduce costs and hardware sizes, digital cameras have recently been developed to possess image sensors for capturing images. A commonly used color filter array (CFA) that covers the image sensors is the Bayer CFA. CFAs direct light to pixels, and each pixel in the image sensor captures the intensity of red, green, or blue light. The light captured by the Bayer CFA is then reconstructed to create full-color images. This reconstruction process is known as demosaicking or CFA interpolation.
This study proposes a highly efficient modified algorithm based on the primary-consistent soft-decision algorithm. This method is characterized according to two features: (1) soft-decision, typical color difference planes (R-G or B-G) are transformed to planes in chrominance spaces (YUV or UV); and (2) instead of using a common median filter, this algorithm employs a false-color reduction process to significantly reduce false color areas that are caused by poor decision. In addition, the experimental results indicated that full-color images, which are processed twice using the proposed method, are visually similar to raw images, and image quality can be enhanced.
In this paper, Section 1 introduces commonly adopted demosaicking algorithms and presents a comparison of their advantages and disadvantages.
Section 2 describes the proposed algorithm. The transformation from color difference planes to planes in chrominance spaces and the false-color reduction process were used to reduce false-color areas that could not be determined by a soft decision. Light is processed twice using these methods to export full-color images that are similar to the raw images.
Section 3 shows the results in which the proposed algorithms visually and statistically improve the image output and quality.
Section 4 provides the evaluation of image errors caused by the image interpolation-restoration method of various algorithms. A small error implies that the quality of the restored images is similar to the raw images. Section 5 offers suggestions for the experiments conducted in this study.
URI: http://hdl.handle.net/11455/9058
其他識別: U0005-2907201316061800
Appears in Collections:電機工程學系所

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