JISP  >> Vol. 6 No. 2 (April 2017)

    一种利用大气散射模型实现图像去雾的方法
    A Method of Image Dehazing Using Atmospheric Scattering Model

  • 全文下载: PDF(1002KB) HTML   XML   PP.78-88   DOI: 10.12677/JISP.2017.62010  
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作者:  

段立春,刘超,钟玮,陈丽清,蒋慕蓉:云南大学,信息学院计算机科学与工程系,云南 昆明

关键词:
图像去雾大气散射模型灭点景深Image Dehazing Atmospheric Scattering Model Vanishing Point Depth of Field

摘要:

针对雾天图像清晰度不高的问题,我们采用大气散射模型进行图像去雾处理,利用Curvelet变换提取图像边缘,根据边缘相交的直线先计算灭点值,再由灭点计算景深值,得到含雾图像中入射光的辐射系数,即图像真实的颜色值,达到图像去雾的目的。在景深值计算中,分别对含雾图像的每个像素点都做一次景深计算,在一定程度上可以解决由于单一景深导致部分区域颜色失真的问题,使去雾后图像的颜色更自然。最后,通过实验验证了方法的有效性。

In view of the problem that the image under foggy days is not clear, the atmospheric scattering model is used to process the image dehazing. First, the Curvelet transform extracts the image edge and calculates the vanishing point on the basis of the intersection of straight line edges. Then it calculates the depth of field value according to the vanishing point. The radiation coefficient of incident light in the foggy image is obtained, which means that the real color value of the image is obtained and the image dehazing is finished. When calculating the depth of field value, it respectively calculates the depth of field value of every foggy image’s pixel at the same time, which solves the partial area color distortion problem that is caused by using single depth of field, and makes the processed image look more natural. Finally, the validity of the method is verified by experiments.

文章引用:
段立春, 刘超, 钟玮, 陈丽清, 蒋慕蓉. 一种利用大气散射模型实现图像去雾的方法[J]. 图像与信号处理, 2017, 6(2): 78-88. https://doi.org/10.12677/JISP.2017.62010

参考文献

[1] 王一涵. 雾天图像增强方法研究[D]: [硕士学位论文]. 西安: 西安电子科技大学, 2012.
[2] Narasimhan, S. and Nayar, S. (2003) Interactive Deweathing of an Image Using Physical Models. Proceeding of IEEE Workshop on Corlor and Photometric Methods in Computer Vision, Chicago, 1-8.
[3] Oakley, J.P. and Satherley, B.L. (1998) Improving Image Quality in Poor Visibility Conditions Using a Physical Model for Contrast Degradation. IEEE Transactions on Image Processing, 7, 167-179.
https://doi.org/10.1109/83.660994
[4] Cozman, F. and Krotkov, E. (1997) Depth from Scattering. Springer, US.
[5] Cantor, A. (1977) Optics of the Atmosphere—Scattering by Molecules and Particles. IEEE Journal of Quantum Elec-tronics, 14, 698-699.
https://doi.org/10.1109/JQE.1978.1069864
[6] John, J. and Wilscy, M. (2008) Enhancement of Weather Degraded Video Sequences Using Wavelet Fusion. 7th IEEE International Conference on IEEE, Cybernetic Intelligent Systems (CIS), London, 9-10 September 2008, 1-6.
https://doi.org/10.1109/ukricis.2008.4798926
[7] Grewe, L. and Brooks, R. (1998) Atmospheric Attenuation Reduction through Multisensor Fusion. Proceedings of the SPIE International Conference Society for Optical Engineering on Sensor Fusion: Architectures, Algorithms and Applications II, Orlando, 102-109.
[8] 杨仁虎. 基于小波分析的数字图像清晰化方法研究[D]: [硕士学位论文]. 成都: 成都理工大学, 2006.
[9] 胡学友. 雾天降质图像的增强复原算法研究[D]: [博士学位论文]. 安徽: 安徽大学, 2011.
[10] 谷秀平. 基于Curvelet变换的图像去噪和增强[D]: [硕士学位论文]. 天津: 天津理工大学, 2010.
[11] Zhang, J.L., Gao, B. and Gu, X.P. (2010) Traffic Images Enhancement Based on Vanishing Point Detection and Atmospheric Scattering Model. Proceedings of 3rd International Congress on Image and Signal Processing, Yantai, 16-18 Octorber 2010, 766-770.
[12] 袁芳. Curvelet变换在数字图像去噪和压缩中的研究[D]: [硕士学位论文]. 西安: 西安电子科技大学, 2010.
[13] Candes, E.J., Demanet, L., Donoho, D.L, et al. (2005) Fast Discrete Curvelet Transforms. Applied and Computational Mathematics, California Institute of Technology, 1-43.
[14] Eriksson, B. (2007) Automatic Image De-Weathering Using Curvelet-Based Vanishing Point Detection. Communications on Pure & Applied Mathematics, 2007, 219-232.
[15] Candes, E.J. (2000) Curvelets, Multiresolution Representation, and Scaling Laws. Proceedings of SPIE—The International Society for Optical Engineering, 4119, 1-12.
https://doi.org/10.1117/12.408568