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Applications in industrial and scientific areas need high-quality multispectral images. However, because of various disadvantages, e.g., adverse environment of sensing, limited spectral bands, etc., multispectral images suffer low contrasts, low signal-noise-ratios, etc. As inputs of applications such as tracking, recognition and so on, multispectral images with low quality may cause those applications to fail. In this paper, aiming to meet practical requirements, we propose an algorithm of efficiently improving miultispectral images. The framework of the proposed method is as follows. According to Retinex theory, a multispectral image can be modeled as multiplicative combination of an illumination component and a reflection component. Typically, an illumination component is of low frequency and determines the dynamic range of pixel intensity, while a reflection component is of high frequency and determines the property of an image. Once we can successfully enhance both an illumination component and a reflection component, which is described later, respectively, we achieve the enhancement of a multispectral image by multiplying two enhanced components. First, we estimate the illumination component based on the principal structures extracted from the multispectral image on a multiple scale, i.e. on a low-level scale, middle-level scale and high-level scale, respectively. The mean value of the three principal structures is used as the estimation of an illumination component. Then the global structure contained in the illumination component, can be obtained by analyzing the corresponding information about its histogram, and is involved to enhance the global contrast and the edge details of the principal structure. Second, with the previously computed illumination component, we can easily derive the reflection component from the multispectral image in pixel-wise division operation. There are adequate image details as well as noise in the reflection component. We suppress the noise and keep the image details by using a non local mean filter. And then we enhance the image details by means of local variances. Finally, multiplying the enhanced illumination component by the filtered reflection component, we enhance the multispectral image. In order to verify the efficiency of our algorithm, experiments are conducted over multispectral image sets including X-ray images, ultraviolet images, well illuminated visible light images, poorly illuminated visible light image, and infrared images. The experimental results show that the proposed algorithm can efficiently remove halo artifacts, well suppress noise and obviously improve local details as well as global contrast. Compared with the state-of-the-art algorithms, the proposed method significantly enhances multispectral images both in vision and in objective analysis by means of information entropy and average gradient.
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Keywords:
- multispectral image enhancement /
- structure extraction /
- Retinex
[1] Bi G L, Xu Z J, Zhao J, Sun Q 2015 Acta Phys. Sin. 64 100701 (in Chinese) [毕国玲, 续志军, 赵健, 孙强 2015 物理学报 64 100701]
[2] Stark J A 2000 IEEE Trans. Image Process. 9 889
[3] Zhao W D, Xu Z J, Zhao J, Zhao F, Han X Z 2014 Infrared Phys. Technol. 66 152
[4] Wu C M 2013 Acta Electronica Sinica 41 598 (in Chinese) [吴成茂 2013 电子学报 41 598]
[5] Tian X P, Cheng X, Wu C M, Liu Y B 2015 Journal of Xi’an University of Posts and Telecommunications 20 51 (in Chinese) [田小平, 程新, 吴成茂, 刘一博 2015 西安邮电大学学报 20 51]
[6] Qi F, Li Y J, Zhang K 2008 Journal of China Ordeance 4 181
[7] Song Q F, L X L, Cheng M X, Lu A J 2014 Electro-Optic Technology Application 29 39 (in Chinese) [宋庆峰, 吕绪良, 隋明序, 卢爱军 2014 光电技术应用 29 39]
[8] Land E H 1986 National Academy Sciences 83 3078
[9] Orsini G, Ramponi G, Carrai P, Federico R D 2003 National Academy Sciences 3 393
[10] Wang Y C, Li S J, Huang L Q 2006 Optics and Precision Engineering 1 70 (in Chinese) [王彦臣, 李树杰, 黄廉卿 2006 光学精密工程 1 70]
[11] Elad M 2005 Scale Space and PDE Methods in Computer Vision 3459 217
[12] Zhao H Y, Xiao C B, Yu J, Dai Y 2014 Journal of Beijing University of Technology 40 404 (in Chinese) [赵宏宇, 肖创柏, 禹晶, 戴岩 2014 北京工业大学学报 40 404]
[13] Yang L, Jiang J 2014 Computer Digital Engineering 42 879 (in Chinese) [杨龙, 姜军 2014 计算机与数字工程 42 879]
[14] Wang B J, Liu S Q, Li Q, Zhou H X 2006 Scale Space and PDE Methods in Computer Vision 48 77
[15] Lai R, Yang Y T, Wang B J, Zhou H X 2010 Opt. Commun. 283 4283
[16] Buades A, Coll B, Morel J M 2005 IEEE Computer Vision and Pattern 38 60
[17] Dimitri V D, Miche K 2009 IEEE Signal Processing Lett. 16 973
[18] Singh S S, Singh T T, Devi H M, Sinam T 2012 Int. J. Comput. Appl. 47 31
[19] Xu L, Yan Q, Xia Y, Jia J Y 2012 ACM Trans. Graphics 31 139
[20] Wang Y Q 2013 Computer Technology and Development 23 63 (in Chinese) [王宇庆 2013 计算机技术与发展 23 63]
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[1] Bi G L, Xu Z J, Zhao J, Sun Q 2015 Acta Phys. Sin. 64 100701 (in Chinese) [毕国玲, 续志军, 赵健, 孙强 2015 物理学报 64 100701]
[2] Stark J A 2000 IEEE Trans. Image Process. 9 889
[3] Zhao W D, Xu Z J, Zhao J, Zhao F, Han X Z 2014 Infrared Phys. Technol. 66 152
[4] Wu C M 2013 Acta Electronica Sinica 41 598 (in Chinese) [吴成茂 2013 电子学报 41 598]
[5] Tian X P, Cheng X, Wu C M, Liu Y B 2015 Journal of Xi’an University of Posts and Telecommunications 20 51 (in Chinese) [田小平, 程新, 吴成茂, 刘一博 2015 西安邮电大学学报 20 51]
[6] Qi F, Li Y J, Zhang K 2008 Journal of China Ordeance 4 181
[7] Song Q F, L X L, Cheng M X, Lu A J 2014 Electro-Optic Technology Application 29 39 (in Chinese) [宋庆峰, 吕绪良, 隋明序, 卢爱军 2014 光电技术应用 29 39]
[8] Land E H 1986 National Academy Sciences 83 3078
[9] Orsini G, Ramponi G, Carrai P, Federico R D 2003 National Academy Sciences 3 393
[10] Wang Y C, Li S J, Huang L Q 2006 Optics and Precision Engineering 1 70 (in Chinese) [王彦臣, 李树杰, 黄廉卿 2006 光学精密工程 1 70]
[11] Elad M 2005 Scale Space and PDE Methods in Computer Vision 3459 217
[12] Zhao H Y, Xiao C B, Yu J, Dai Y 2014 Journal of Beijing University of Technology 40 404 (in Chinese) [赵宏宇, 肖创柏, 禹晶, 戴岩 2014 北京工业大学学报 40 404]
[13] Yang L, Jiang J 2014 Computer Digital Engineering 42 879 (in Chinese) [杨龙, 姜军 2014 计算机与数字工程 42 879]
[14] Wang B J, Liu S Q, Li Q, Zhou H X 2006 Scale Space and PDE Methods in Computer Vision 48 77
[15] Lai R, Yang Y T, Wang B J, Zhou H X 2010 Opt. Commun. 283 4283
[16] Buades A, Coll B, Morel J M 2005 IEEE Computer Vision and Pattern 38 60
[17] Dimitri V D, Miche K 2009 IEEE Signal Processing Lett. 16 973
[18] Singh S S, Singh T T, Devi H M, Sinam T 2012 Int. J. Comput. Appl. 47 31
[19] Xu L, Yan Q, Xia Y, Jia J Y 2012 ACM Trans. Graphics 31 139
[20] Wang Y Q 2013 Computer Technology and Development 23 63 (in Chinese) [王宇庆 2013 计算机技术与发展 23 63]
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