红外伪彩色图像的去彩化研究

杨晨; 张智勇; 王继红; 黄寓洋; 丁召

doi:10.5768/JAO201536.0302004

红外伪彩色图像的去彩化研究

doi: 10.5768/JAO201536.0302004

1.
贵州大学贵州省微纳电子与软件技术重点实验室,贵州贵阳 550025;
2.
中国科学院苏州纳米技术与纳米仿生研究所,江苏苏州 215125

详细信息

通讯作者:
杨晨（1982-），男，贵州贵阳人，博士，主要从事光电系统与嵌入式成像技术研究。Email:eliot.c.yang@163.com

中图分类号: TN911.73
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出版历程

Decolorizing of infrared pseudo color image

1.
Guizhou Provincial Key Laboratory for Micro-Nano-Electronics and Software,Guizhou University,Guiyang 550025,China;
2.
Suzhou Institute of Nano-tech and Nano-bionics,CAS,Suzhou 215125,China

摘要

摘要: 为适应人眼的感知能力，伪彩色编码技术常被用于处理红外探测系统输出的灰度图像。较于灰度图像，计算机对彩色图像处理所需的存储量与计算量较大。鉴于某些红外设备只提供伪红外图像的输出，因此伪红外图像的去彩色化算法成为该文的主要研究目标。除利用最大值分解、平均值与光亮度法3种常见灰度化算法对伪红外图像的处理外，直接对伪彩色图像进行解码的方式也在文中被用于红外图像的去彩色化。实验基于上述两类方法，对线性与正弦非线性彩虹伪彩色图像的去彩色化进行了研究,并对处理后图像的质量进行了比较。实验结果表明，常规灰度算法处理后的伪彩色图像质量较差，而线性伪彩色图像经解码处理后可完全恢复出原图，实现0均方差；对于正弦非线性算法，虽然存在由量化误差引起的损失，但与原图像比仍有非常低的均方差（0.309 4）与高的峰值信噪比（101.356 0）。
- 伪彩色图像 /
- 解码 /
- 非线性 /
- 量化误差
Abstract: Considering the human eyes- perception capability, the pseudo color coding technology is usually adopted to colorize the output gray image of infrared systems. Comparing to gray images, computers need more memory and calculation to process color images. Since some infrared equipments only provide pseudo color images,the grayscale conversion of pseudo color image was studied. Besides the 3 common algorithms, maximum decomposition, average and luminance, a decoding method was presented to decolorize infrared images. Based on the 2 kinds of methods above, decolorizing for both linear and sinusoidal nonlinear pseudo color images were studied in experiment; furthermore, image quality was also compared. The experiment result demonstrates that the decoding method is a kind of lossless algorithm for linear pseudo color image with 0 mean squared error. For sinusoidal nonlinear algorithm, loss due to quantization errors are inevitable, it still has a quite low mean square error (0.309 4) and high peak signal noise ratio (101.356 0).
- pseudo color image /
- decode /
- nonlinear /
- quantization error

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参考文献(1)

[1]Gonzalez R C, Woods R E. Digital image processing[M]. 3rd ed. Indiana： Prentice Hall, 2007：29-46.
[2]Levkowitz H, Herman G T. The design and evaluation of color scales for image data [J]. IEEE computer Graphics and Applications, 1992, 12(1): 72-80.
[3]Wu Qiang, Wang Xinsai, Wang Weiping, et al. Infrared imaging colorizing method based on local linear embedding [M]. Journal of Applied Optics, 2012, 33(4): 733-737.
吴强，王新赛，王卫平, 等. 基于LLE的红外成像彩色化处理算法研究[M]. 应用光学，2012，33(4): 733-737.
[4]Liu Chanlao, Tan Lixun, Li Chunyang, et al. Pseudo-color coding and processing for infrared images [J]. Jounal of Applied Optics,2006,27(5):419-422.
刘缠牢, 谭立勋, 李春燕,等. 红外图像伪彩色编码和处理[J]. 应用光学，2006, 27(5): 419-422.
[5]Saravanan C. Color image to grayscale image conversion [C]. Washington：Computer Engineering and Applications (ICCEA), 2010 Second International Conference, 2010: 196-199.
[6]Kanan C, Cottrell G W. Color-to-crayscale: does the method matter in image recognition? [J]. PLoS ONE, 2012, 7(1): e29740.
[7]Smith K, Landes P E, Thollot J, et al. Apparent greyscale: a simple and fast conversion to perceptually accurate images and video [J]. Computer Graphics Forum, 2008, 27(2): 193-200.
[8]Hua X. Human computer interactions for converting color images to gray [J]. Neurocomputing, 2012, 85: 1-5.
[9]Abidi B R, Zheng Y, Gribok A V, et al. Improving weapon detection in single energy X-Ray images through pseudocoloring [J]. IEEE,2006,36(6):748-196.
[10]Gooch A A, Olsen S C, Tumblin J, et al. Color2Gray: salience-preserving color removal [J]. ACM Transactions on Graphics, 2005, 24(3): 634-639.
[11]Reinhard E, Ashikhmin M, Gooch B, et al. Color transfer between images [J]. IEEE, 2001, 21(5): 34-41.
[12]Grundland M, Dodgson N A. Decolorize: Fast, contrast enhancing, color to grayscale conversion [J]. Pattern Recognition, 2007, 40 (11): 2891-2896.
[13]Huynh-Thu Q, Ghanbari M. Scope of validity of PSNR in image/video quality assessment [J]. Electronics Letters, 2008, 44(13): 800-801.
[14]Chikkerur S, Sundaram V, Reisslein M, et al. Objective video quality assessment methods: a classification, review, and performance comparison [J]. IEEE, 2011, 57(2): 165-182.

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