Fusion and vision algorithm of spectral data based on mapping-evaluating-optimizing methods within multi-section
-
摘要: 研究降维、去冗后光谱数据色彩显示问题。传统的光谱数据色彩显示时，常采用截取或压缩至0~1范围进行映射，容易丢失图像细节，提出一种基于多区间平移映射评价优选方法的光谱数据色彩融合显示算法。首先对光谱数据立方体进行主成份变换，将前三成分分别赋值给对色空间的黑白通道、红绿通道和黄蓝通道，然后经过空间变换到sRGB空间，将数据分段平移到0~1范围，映射至8位RGB空间，并对每次平移映射图像进行标准差、熵、平均梯度的单项评价，全部平移结束后，对所有的评价值进行综合评价，选取综合评价值最高的区间输出映射。实验结果表明，融合图像能最大限度地保证图像的能量、信息和清晰度，有利于人眼的快速识别判断。Abstract: Color vision of spectral data after reducing dimension and redundancy was researched. The traditional mapping method, cutting or compressing data into the range between 0 and 1, can always lead to loss of the fusion image details. A novel fusion and vision algorithm of spectral data was presented, based on the shifting-mapping-evaluating-optimizing way within multi-section. The first 3 principal component values were achieved by principal component transform (PCT) of the spectral data cube, and assigned respectively to white-black, red-green and yellow-blue channels of opponent color space. The values of standard red-green-blue (sRGB) color space were transformed from opponent color space, and the sRGB values were divided into several sections, moved to the range of 0 to 1 and mapped to 8bit RGB digital code values. The single item evaluations of standard deviation, entropy, average gradient were calculated from 8bit RGB. The comprehensive evaluation values were got from all of the single item evaluation after finishing the process of moving, mapping and single item evaluation. The fusion image was mapped at the section with maximum comprehensive evaluation. The results show that the fusion image can ensure the images energy, information and definition, which is useful for manual distinguish and judge rapidly.
-
Keywords:
- spectroscopy /
- fusion and vision /
- evaluating and optimizing /
- data cube /
- imaging spectra /
- color mapping
-
-
[1]Bratkova M, Boulos S, Shirley P. oRGB: A practical opponent color space for computer graphics[J]. IEEE Computer Graphics and Applications, 2009, 29(1): 42-55.
[2]Johnson G M, Song X, Montag E D, et al. Derivation of a color space for image color difference measurement[J]. Color Research and Application, 2010, 35(6): 387-400.
[3]Reinhard E, Ashikhmin M, Gooch B, et al. Color transfer between images[J]. IEEE Computer Graphics and Applications, 2001, 21(5): 2-9.
[4]Livitz G, Yazdanbakhsh A, Eskew R T, et al. Perceiving opponent hues in color induction displays[J]. Seeing and Perceiving, 2011, 24(1):1-17.
[5]Tyo J S,Konsolakis A, Diersen D I.Richard christopher olsen:principal-components-based display strategy for spectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(3): 1-12.
[6]Stokes M, Anderson M. Chandrasekars, et al. A standard default color space for the internet: sRCB［EB/OL］. http://www.color.org/sRGB.xalter. [1996-11-5].version:1-10.
[7]Fairchild M D. Color appearance models[M]. 3nd ed. West Sussex: John Wiley & Sons, 2013.
[8]Achalakul T, Taylor S. A concurrent spectral-screening PCT algorithm for remote sensing application[J]. Journal of Information Fusion, 2000,(1/2): 89-97.
[9]Chen Dake. Fusion algorithm of multispectral and panchromatic images[D]. Jilin: Jilin University,2010.
陈大可. 多光谱与全色图像融合方法的研究[D]. 吉林: 吉林大学, 2010.
[10]Yang Fanglin, Guo Hongyang, Yang Fengbao. Study of evaluation methods on effect of pixel-level image fusion[J]. Journal of Test And Measurement Technology, 2002, 16(4): 276-279.
阳方林, 郭红阳, 杨风暴. 像素级图像融合效果的评价方法研究[J]. 测试技术学报, 2002, 16(4): 276-279.
[11]Zhang Mingxuan, Gao Jiaobo, Meng Hemin, et al. Zoom-FFT based on Fourier transform spectroscopy[J]. Journal of Applied Optics, 2013, 34(3): 452-456.
张茗璇, 高教波, 孟合民, 等. 基于傅里叶变换光谱技术的Zoom-FFT算法研究[J]. 应用光学,2013,34(3): 452-456.
[12]Li Yu, Gao Jiaobo, Meng Hemin, et al. Fast inversion techniques of inteferogram imaging spectrum base on CUDA[J]. Journal of Applied Optics, 2014,35(3): 414-419.
李宇, 高教波, 孟合民, 等. 基于统一计算设备架构的干涉成像光谱快速反演技术研究[J]. 应用光学,2014,35(3): 414-419.
计量
- 文章访问数: 1461
- HTML全文浏览量: 88
- PDF下载量: 125
下载:
陕公网安备 61011302001501号 