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为解决多谱段降质图像增强问题，提出了一种基于光照-反射成像模型和形态学操作的多谱段图像增强算法.首先对图像饱和度使用自适应非线性拉伸函数进行拉伸，使增强后的图像色彩更加饱和、自然；接下来利用引导滤波算法提取出图像的光照分量，提出了一种基于细节特征的加权融合策略，利用光照分布特性构造了一种自适应Gamma校正函数对光照分量进行处理，并将其与利用对比度受限的自适应直方图均衡化方法处理后的光照分量以及原始光照分量进行融合；然后在反射分量校正时，构造了一种形态学操作函数来校正反射信息；最后合并光照分量和反射分量，并与处理后的饱和度分量和色调分量一起得到增强图像.采用主客观评价指标对可见光低照度图像、水下图像、高动态范围图像、沙尘暴图像、雾天图像和热红外图像6种降质多谱段图像实验结果进行分析比较，结果表明本文算法能够有效地抑制图像噪声、增强图像细节信息、改善图像视觉效果，可应用于多种图像增强领域.In this paper we propose a multispectral image enhancement algorithm based on illuminance-reflection imaging model and morphology operation that enables us to solve the problem of improving the multispectral degraded images. Firstly, we transform the image from RGB space to HSV color space, and the hue remains unchanged. As for the saturation component, we use the adaptive nonlinear stretching to improve the image color saturation and brightness. Secondly, according to the illuminance-reflection imaging model, we adopt the guided image filtering method to decompose the brightness into illuminance component and reflection component. Usually, the illumination component mainly determines the dynamic range of the pixels in the image, corresponding to the low frequency part of the image, reflecting the global characteristics of the image and the edge detail information of the image; the reflected component represents the intrinsic essential characteristics of the image, corresponding to the high frequency part of the image, and contains most of the local detail information of the image as well as all noise. Thirdly, we present an improved adaptive gamma function, which can dynamically adjust the illuminance component by the local distribution characteristics, and use the contrast-limited adaptive histogram equalization to correct the illuminance component. Afterwards we propose a detail-feature weighted fusion strategy. The original illumination and the two corrected illuminations are fused to obtain the final illumination component. Fourthly, we propose an improved morphological operation to denoise and enhance the details of the reflection component. Finally, the corrected illumination component and the enhanced reflection component are combined to obtain the improved brightness component. In order to verify the efficiency of the algorithm proposed in the paper, we use both subjective visual effectiveness method and quantitative parameter analysis method to measure the enhancement performance in multispectral imaging scenarios, including low illumination image, underwater image, high-dynamic range image, sandstorm image, haze image and thermal infrared image. Then standard deviation, information entropy and average gradient are used as evaluation indices respectively, and qualitative and quantitative comparison with a variety of image enhancement algorithms show that the proposed algorithm can not only well suppress noise but also obviously improve local details and global contrast. Experimental results show that the proposed method proves to be better in performance.
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Keywords:
- multispectral image enhancement /
- detailed-features /
- guided image filter /
- morphological operation
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[19] Song R X, Li D, Wang X C 2017 J. Graphics 38 217 (in Chinese)[宋瑞霞, 李达, 王小春 2017 图学学报 38 217]
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[21] Li M, Liu J, Yang W, Sun X, Guo Z 2018 IEEE Trans. Image Process. 27 2828
[22] Ying Z, Li G, Ren Y, Wang R, Wang W 2017 IEEE International Conference on Computer Vision Workshop, IEEE Computer Society Venice, Italy, October 22-29, 2017 p3015
[23] Fu X, Liao Y, Zeng D, Huang Y, Zhang X P, Ding X 2015 IEEE Trans. Image Process. 24 4965
[24] Guo X, Li Y, Ling H 2017 IEEE Trans. Image Process. 26 982
[25] Fu X, Zeng D, Huang Y, Liao Y, Ding X, Paisley J 2016 Signal Process. 129 82
[26] Lu B P, Li Y J, Zheng Y M, Wang Y K 2017 J. Chin. Mini-Micro Comput. Syst. 38 625 (in Chinese) [芦碧波, 李玉静, 郑艳梅, 王玉琨 2017 小型微型计算机系统 38 625]
[27] Reinhard E, Stark M, Shirley P, Ferwerda J 2002 Conference on Computer Graphics & Interactive Techniques San Antonio, Texas, USA, July 21-26, 2002 p267
[28] Cai B, Xu X, Jia K, Qing C, Tao D 2016 IEEE Trans. Image Process. 25 5187
[29] Berman D, Treibitz T, Avidan S 2016 IEEE Conference on Computer Vision and Pattern Recognition Las Vegas, NV, USA, June 27-30, 2016 p1674
[30] Fu X, Huang Y, Zeng D, Zhang X P, Ding X 2014 IEEE International Workshop on Multimedia Signal Processing Jakarta, Indonesia, September 22-24, 2014 p1
[31] Li W, Yi B, Huang T, Yao W, Peng H 2016 KSⅡ Trans. Internet Inf. 10 1846
[32] Lee S 2007 IEEE Trans. Circ. Syst. Vid. 17 199
[33] Chen X H, Wu W, Meng S Y, Li M Y 2014 Chin. Phys. B 23 90701
[34] Ji D J, Qu G R, Hu C H, Liu B D, Jian J B, Guo X K 2017 Chin. Phys. B 26 60701
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[1] Chen X H, Yan L, Wu W, Meng S Y, Wu L A, Sun Z B, Wang C, Zhai G J 2017 Chin. Phys. B 26 60702
[2] Lu X W, Li J Z, Chen H Y 2010 Chin. Phys. Lett. 27 104209
[3] 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 64 100701]
[4] Li H, Wu W, Yang X M, Yan B Y, Liu K, Jeon G 2016 Acta Phys. Sin. 65 160701 (in Chinese)[李红, 吴炜, 杨晓敏, 严斌宇, 刘凯, Gwanggil Jeon 2016 65 160701]
[5] Sim K S, Tso C P, Tan Y Y 2007 Pattern Recogn. Lett. 28 1209
[6] Sasi N M, Jayasree V K 2013 Engineering 5 326
[7] Chen Q, Bai L H, Zhang B M 2003 J. Infrared Millim. Waves 22 428 (in Chinese)[陈钱, 柏连发, 张保民 2003 红外与毫米波学报 22 428]
[8] Jobson D J, Rahman Z, Woodell G A 1997 IEEE Trans. Image Process. 6 451
[9] Rahman Z, Jobson D J, Woodell G A 2002 International Conference on Image Processing Lausanne, Switzerland, September 19, 1996 p1003
[10] Jobson D J, Rahman Z, Woodell G A 1997 IEEE Trans. Image Process. 6 965
[11] Fu X, Zhuang P, Huang Y, Liao Y, Zhang X P, Ding X 2015 IEEE International Conference on Image Processing Quebec City, Canada, Septmber 27-30, 2014 p4572
[12] He K, Sun J, Tang X 2013 IEEE Trans. Pattern Anal. 35 1397
[13] Li Z, Zheng J, Zhu Z, Yao W, Wu S 2015 IEEE Trans. Image Process. 24 120
[14] Li H P 2014 M. S. Thesis (Kunming: Yunnan University) (in Chinese)[李红平2014 硕士学位论文 (昆明: 云南大学)]
[15] Durand F, Dorsey J 2002 Acm Trans. Graphic 21 257
[16] He K, Sun J, Tang X 2011 IEEE Trans. Pattern Anal. 33 2341
[17] Li H Y, Zhong M Z 2016 J. Kashgar Univ. 37 42 (in Chinese)[李海燕, 钟梦之 2016 喀什大学学报 37 42]
[18] Fu X, Zeng D, Huang Y, Ding X, Zhang X P 2014 IEEE Global Conference on Signal and Information Processing Austin, TX, USA, December 3-5, 2013 p1085
[19] Song R X, Li D, Wang X C 2017 J. Graphics 38 217 (in Chinese)[宋瑞霞, 李达, 王小春 2017 图学学报 38 217]
[20] Ying Z, Li G, RenY, Wang R, Wang W 2017 Computer Analysis of Images and Patterns 17th International Conference Proceedings, Part II Ystad, Sweden, August 22-24, 2017 p36
[21] Li M, Liu J, Yang W, Sun X, Guo Z 2018 IEEE Trans. Image Process. 27 2828
[22] Ying Z, Li G, Ren Y, Wang R, Wang W 2017 IEEE International Conference on Computer Vision Workshop, IEEE Computer Society Venice, Italy, October 22-29, 2017 p3015
[23] Fu X, Liao Y, Zeng D, Huang Y, Zhang X P, Ding X 2015 IEEE Trans. Image Process. 24 4965
[24] Guo X, Li Y, Ling H 2017 IEEE Trans. Image Process. 26 982
[25] Fu X, Zeng D, Huang Y, Liao Y, Ding X, Paisley J 2016 Signal Process. 129 82
[26] Lu B P, Li Y J, Zheng Y M, Wang Y K 2017 J. Chin. Mini-Micro Comput. Syst. 38 625 (in Chinese) [芦碧波, 李玉静, 郑艳梅, 王玉琨 2017 小型微型计算机系统 38 625]
[27] Reinhard E, Stark M, Shirley P, Ferwerda J 2002 Conference on Computer Graphics & Interactive Techniques San Antonio, Texas, USA, July 21-26, 2002 p267
[28] Cai B, Xu X, Jia K, Qing C, Tao D 2016 IEEE Trans. Image Process. 25 5187
[29] Berman D, Treibitz T, Avidan S 2016 IEEE Conference on Computer Vision and Pattern Recognition Las Vegas, NV, USA, June 27-30, 2016 p1674
[30] Fu X, Huang Y, Zeng D, Zhang X P, Ding X 2014 IEEE International Workshop on Multimedia Signal Processing Jakarta, Indonesia, September 22-24, 2014 p1
[31] Li W, Yi B, Huang T, Yao W, Peng H 2016 KSⅡ Trans. Internet Inf. 10 1846
[32] Lee S 2007 IEEE Trans. Circ. Syst. Vid. 17 199
[33] Chen X H, Wu W, Meng S Y, Li M Y 2014 Chin. Phys. B 23 90701
[34] Ji D J, Qu G R, Hu C H, Liu B D, Jian J B, Guo X K 2017 Chin. Phys. B 26 60701
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