基于融合与高斯加权暗通道的单幅图像去雾算法
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摘要
针对图像去雾算法在景深突变处出现光晕现象和远景区域去雾不足的问题,提出了一种基于融合与高斯加权暗通道的单幅图像去雾算法.利用图像形态学梯度的特点,将形态学梯度图像与暗通道图像线性融合获取融合暗通道,构造自适应的高斯权重参数对融合的暗通道图像逐像素处理获取粗透射率,在使用L1正则化优化透射率,通过大气散射模型与修复的大气光值恢复无雾图像.仿真实验表明,本文算法可以较好地恢复出图像的细节并抑制光晕现象,与几种典型的图像去雾算法客观对比,证实了本文算法的可行性.
Aiming at the problem that the image dehazing algorithm has halo phenomenon in depth discontinuity and legacy residual fog in the distant area,this paper proposes a single image dehazing algorithm based on fusion and Gaussian weighted dark channel.Firstly,using the characteristics of image morphology gradient,the morphological gradient image and the dark channel image are linearly fused to obtain the fusion dark channel.Secondly,the adaptive Gaussian weight parameter is constructed to pixelby-pixel process the fused dark channel image to obtain the coarse transmission,and the L1 regularization is used to optimize the transmission.Finally,the haze-free image is restored by the atmospheric scattering model and the restored atmospheric light value.Experimental results show that the proposed algorithm can recover the details of the image and suppress the halo phenomenon.The objective comparison with several typical algorithms proves the feasibility of the proposed algorithm.
Aiming at the problem that the image dehazing algorithm has halo phenomenon in depth discontinuity and legacy residual fog in the distant area,this paper proposes a single image dehazing algorithm based on fusion and Gaussian weighted dark channel.Firstly,using the characteristics of image morphology gradient,the morphological gradient image and the dark channel image are linearly fused to obtain the fusion dark channel.Secondly,the adaptive Gaussian weight parameter is constructed to pixelby-pixel process the fused dark channel image to obtain the coarse transmission,and the L1 regularization is used to optimize the transmission.Finally,the haze-free image is restored by the atmospheric scattering model and the restored atmospheric light value.Experimental results show that the proposed algorithm can recover the details of the image and suppress the halo phenomenon.The objective comparison with several typical algorithms proves the feasibility of the proposed algorithm.
引文
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[20] HAUTIERE N.Blind contrast enhancement assessment by gradient ratioing at visible edges[J].Image Analysis&Stereology Journal,2008,27(2):87-95.
[2] WANG W,YUAN X.Recent advances in image dehazing[J].IEEE/CAA Journal of Automatica Sinica,2017,4(3):410-436.
[3] YANG Yan,CHEN Gao-ke.Single image visibility restoration using optical compensation and pixel-by-pixel transmission estimation[J].Journal on Communications,2017,38(5):48-56.杨燕,陈高科.基于光补偿和逐像素透射率的图像复原算法[J].通信学报,2017,38(5):48-56.
[4] TAN R T.Visibility in bad weather from a single image[C].Proceeding of IEEE Conference on Computer Vision and Pattern Recognition.Anchorage,AK:IEEE Press,2008:1-8.
[5] FATTAL R.Single image dehazing[J].ACM Transactions on Graphics,2008,27(3):1-9.
[6] HE K M,SUN J,TANG X O.Single image haze removal using dark channel prior[J].IEEE Transactions on Pattern8-2000110Analysis and Machine Intelligence,2011,33(12):2341-2353.
[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] TAREL J P,HAUTIERE N.Fast visibility restoration from a single color or gray level image[C].International Conference on Computer Vision,IEEE,2010:2201-2208.
[9] MENG G F,WANG Y,DUAN J,et al.Efficient image dehazing with boundary constraint and contextual regularization[C].IEEE International Conference on Computer Vision.IEEE,2014:617-624.
[10] SUN W,LI D J,LIU H J,et al.Fast single image fog removal based on atmospheric scattering model[J].Optics&Precision Engineering,2013,21(4):1040-1046.
[11] CAI B,XU X,JIA K,et al.An end-to-end system for single image haze removal[J].IEEE Transactions on Image Processing,2016,25(11):5187-5198.
[12] REN W Q,LIU S,ZHANG H,et al.Single image dehazing via multi-scale convolutional neural networks[M].Computer Vision-ECCV 2016,Springer International Publishing,2016:154-169.
[13] TANG Qun-fang,YANG Jie,LIU Hai-bo,et al.Fast single-image dehazing method based on dark channel prior[J].Acta Photonica Sinica,2017,46(9):0910001.汤群芳,杨杰,刘海波,等.基于暗通道先验的单幅图像快速去雾方法[J].光子学报,2017,46(9):0910001.
[14] MCCARTNEY E J.Optics of the atmosphere:scattering by molecules and particles[M].New York,John Wiley and Sons,Inc,1976:421-421.
[15] LEVIN A,LISCHINSKI D,WEISS Y.A closed form solution to natural image matting[C].Computer Vision and Pattern Recognition,2006IEEE Computer Society Conference on IEEE,2006:61-68.
[16] YU T,RIAZ I,PIAO J,et al.Real-time single image dehazing using block-to-pixel interpolation and adaptive dark channel prior[J].Iet Image Processing,2015,9(9):725-734.
[17] NAMER E,SCHECHNER Y Y.Advanced visibility improvement based on polarization filtered images[J].Proceedings of the 2005 Polarization Science and Remote Sensing,2005:36-45.
[18] KIM J H,JANG W D,SIM J Y,et al.Optimized contrast enhancement for real-time image and video dehazing[J].Journal of Visual Communication&Image Representation,2013,24(3):410-425.
[19] 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.
[20] HAUTIERE N.Blind contrast enhancement assessment by gradient ratioing at visible edges[J].Image Analysis&Stereology Journal,2008,27(2):87-95.