基于边界限制加权最小二乘法滤波的雾天图像增强算法
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摘要
针对经典的暗通道理论算法在处理雾天图像时天空区域出现光晕和亮度损失的问题,提出了一种基于边界限制加权最小二乘法滤波的雾天图像增强算法。该方法根据雾天图像的直方图特性,分割出天空区域,并求解出了全局大气背景光;根据辐射立方体法则推导出边界限制条件,得到了初始的透射率,运用加权最小二乘法滤波方法和容差机制,对透射率进行了平滑处理;利用暗通道理论的模型,求取了增强后的图像。研究结果表明,在去雾效果和图像的可视度方面,所提算法优于现有的暗通道算法。
Aiming at the problems of image hue and brightness distortion in sky regions when dealing with fog images by the classic dark channel theory algorithm, we propose a fog image enhancement algorithm based on the boundary constraint weighted least squares filtering. According to the histogram property of fog image, we reduce the boundary condition and obtain the initial transmittance. The transmission is smoothed by weighted least squares filtering method and tolerance mechanism. The enhanced image is obtained by using the model of dark channel theory. The research results show that the proposed algorithm is better than the existing dark channel algorithm in terms of dehazing effect and image visibility.
Aiming at the problems of image hue and brightness distortion in sky regions when dealing with fog images by the classic dark channel theory algorithm, we propose a fog image enhancement algorithm based on the boundary constraint weighted least squares filtering. According to the histogram property of fog image, we reduce the boundary condition and obtain the initial transmittance. The transmission is smoothed by weighted least squares filtering method and tolerance mechanism. The enhanced image is obtained by using the model of dark channel theory. The research results show that the proposed algorithm is better than the existing dark channel algorithm in terms of dehazing effect and image visibility.
引文
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[14] Meng G F, Wang Y,Duan J Y, et al. Efficient image dehazing with boundary constraint and contextual regularization[C]//2013 IEEE International Conference on Computer Vision, December 1-8, 2013, Sydney, NSW, Australia. New York: IEEE, 2013: 617-624.
[15] Farbman Z, Fattal R, Lischinski D, et al. Edge-preserving decompositions for multi-scale tone and detail manipulation[J]. ACM Transactions on Graphics, 2008, 27(3): 1-10.
[16] Zhu Q S, Mai J M, Shao L. A fast single image haze removal algorithm using color attenuation prior[J]. IEEE Transactions on Image Processing, 2015, 24(11): 3522-3533.
[17] Cai B L, Xu X M, Jia K, et al. DehazeNet: an end-to-end system for single image haze removal[J]. IEEE Transactions on Image Processing, 2016, 25(11): 5187-5198.
[18] Zhang S D, He F Z, Yao J. Single image dehazing using deep convolution neural networks[M]//Zeng B, Huang Q, El Saddik A, et al. eds. Advances in Multimedia Information Processing-PCM 2017. Cham: Springer International Publishing, 2018: 128-137.
[19] Hautière N, Tarel J P, Aubert D, et al. Blind contrast enhancement assessment by gradient ratioing at visible edges[J]. Image Analysis & Stereology, 2011, 27(2): 87-95.
[20] Choi L K, You J, Bovik A C. Referenceless prediction of perceptual fog density and perceptual image defogging[J]. IEEE Transactions on Image Processing, 2015, 24(11): 3888-3901.
[2] Narasimhan S G, Nayar S K. Vision and the atmosphere[J]. International Journal of Computer Vision, 2002, 48(3): 233-254.
[3] Jobson D J, Rahman Z, Woodell G A. Properties and performance of a center/surround Retinex[J]. IEEE Transactions on Image Processing, 1997, 6(3): 451-462.
[4] Rahman Z U, Jobson D J, Woodell G A. Retinex processing for automatic image enhancement[J]. Journal of Electronic Imaging, 2004, 13(1): 100-110.
[5] Elad M, Kimmel R, Shaked D, et al. Reduced complexity Retinex algorithm via the variational approach[J]. Journal of Visual Communication and Image Representation, 2003, 14(4): 369-388.
[6] Narasimhan S G. Interactive deweathering of an image using physical model[C]//IEEE Workshop on Color and Photometric Methods in Computer Vision, October 13-16, 2003, Beijing, China. New York: IEEE, 2013: 1-8.
[7] He K M, Sun J, Tang X O. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397-1409.
[8] He K M, Sun J, Tang X O. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12): 2341-2353..
[9] Fattal R. Single image dehazing[J]. ACM Transactions on Graphics, 2008, 27(3): 1-9.
[10] Yu J, Liao Q M. Fast single image fog removal using edge-preserving smoothing[C]//2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 22-27, 2011, Prague, Czech Republic. New York: IEEE, 2011: 1245-1248.
[11] Lu H M, Li Y J,Serikawa S. Underwater image enhancement using guided trigonometric bilateral filter and fast automatic color correction[C]//2013 IEEE International Conference on Image Processing, September 15-18, 2013, Melbourne, VIC, Australia. New York: IEEE, 2013: 3412-3416.
[12] M. K. Saggu1 and S. Singh. Different techniques to dehaze an underwater image[J]. International Journal for Technological Research in Engineering, 2015, 2(6): 566-568.
[13] Gao Y, Yun L J, Shi J S, et al. Enhancement dark channel algorithm of fog image based on the TV model[J]. Chinese Journal of Lasers, 2015, 42(8): 0809001. 高银, 云利军, 石俊生, 等. 基于TV模型的暗原色理论雾天图像复原算法[J]. 中国激光, 2015, 42(8): 0809001.
[14] Meng G F, Wang Y,Duan J Y, et al. Efficient image dehazing with boundary constraint and contextual regularization[C]//2013 IEEE International Conference on Computer Vision, December 1-8, 2013, Sydney, NSW, Australia. New York: IEEE, 2013: 617-624.
[15] Farbman Z, Fattal R, Lischinski D, et al. Edge-preserving decompositions for multi-scale tone and detail manipulation[J]. ACM Transactions on Graphics, 2008, 27(3): 1-10.
[16] Zhu Q S, Mai J M, Shao L. A fast single image haze removal algorithm using color attenuation prior[J]. IEEE Transactions on Image Processing, 2015, 24(11): 3522-3533.
[17] Cai B L, Xu X M, Jia K, et al. DehazeNet: an end-to-end system for single image haze removal[J]. IEEE Transactions on Image Processing, 2016, 25(11): 5187-5198.
[18] Zhang S D, He F Z, Yao J. Single image dehazing using deep convolution neural networks[M]//Zeng B, Huang Q, El Saddik A, et al. eds. Advances in Multimedia Information Processing-PCM 2017. Cham: Springer International Publishing, 2018: 128-137.
[19] Hautière N, Tarel J P, Aubert D, et al. Blind contrast enhancement assessment by gradient ratioing at visible edges[J]. Image Analysis & Stereology, 2011, 27(2): 87-95.
[20] Choi L K, You J, Bovik A C. Referenceless prediction of perceptual fog density and perceptual image defogging[J]. IEEE Transactions on Image Processing, 2015, 24(11): 3888-3901.
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