消除光晕效应和保持细节信息的图像快速去雾算法
|
摘要
针对现有的基于大气散射物理模型的图像去雾算法在去雾过程中大多无法避免地会产生光晕效应和细节丢失,提出了一种消除光晕效应和保持细节信息的图像快速去雾算法。首先运用四叉树子矩阵划分的分层遍历方法得到更精确的大气光值,再通过分析大气散耗函数,利用融合梯度信息的改进引导滤波得到精确估计的大气散耗函数,并自适应地获取最小值图像与大气光平均值的阈值,求解出透射图;最后反演复原出无雾图像,并对复原后的图像进行亮度调整。对多组有雾图像进行了实验,该算法能有效地抑制去雾过程中产生的光晕效应,较多地保留了图像的细节信息,且运行时间大约减少了一倍。融合梯度信息的改进引导滤波不但可以较好地保留透射图的细节信息,有效地消除光晕效应,而且具有较好的鲁棒性和时间复杂性,适用于交通等室外场景的去雾。
Due to the existing image defogging algorithm based on atmospheric scattering model,the defogging process would inevitablely have a halo effect and details were lost,this paper proposed a fast defogging algorithm for eliminating halo effect and keep the detail information of the image. The algorithm first used space division level method based on four tree sub to get more accurate atmospheric light value. Through analysis of the atmospheric dispersion consumption function,it used the improved gradient information fusion filter to get the accurate estimation of atmospheric dispersion consumption function,and adaptively got the threshold of image minimum and average value of atmospheric optical,and calculated the transmission map.Finally it inversion recovered no fog image,and adjusted the brightness of the restored image. Experiment results show the algorithm can effectively suppress the halo effect in fog removal,and can preserve more details and reduce the running time by about twice. The improved guide filter by fusion gradient information can better eliminate the halo effect effectively,and has better robustness and time complexity. This algorithm is suitable for transportation and other outdoor scenes to defog.
Due to the existing image defogging algorithm based on atmospheric scattering model,the defogging process would inevitablely have a halo effect and details were lost,this paper proposed a fast defogging algorithm for eliminating halo effect and keep the detail information of the image. The algorithm first used space division level method based on four tree sub to get more accurate atmospheric light value. Through analysis of the atmospheric dispersion consumption function,it used the improved gradient information fusion filter to get the accurate estimation of atmospheric dispersion consumption function,and adaptively got the threshold of image minimum and average value of atmospheric optical,and calculated the transmission map.Finally it inversion recovered no fog image,and adjusted the brightness of the restored image. Experiment results show the algorithm can effectively suppress the halo effect in fog removal,and can preserve more details and reduce the running time by about twice. The improved guide filter by fusion gradient information can better eliminate the halo effect effectively,and has better robustness and time complexity. This algorithm is suitable for transportation and other outdoor scenes to defog.
引文
[1]禹晶,徐东彬,廖庆敏.图像去雾技术研究进展[J].中国图象图形学报,2011,16(9):1561-1576.(Yu Jing,Xu Dongbin,Liao Qingmin.Image defogging:a survey[J].Journal of Image and Graphics,2011,16(9):1561-1576.)
[2]朱淼良,钱徽.自然景物中大气退化模型的研究[J].计算机辅助设计与图形学学报,2001,13(9):793-799.(Zhu Miaoliang,Qian Hui.Exploring atmospheric degradation model of landscape[J].Journal of Computer-Aided Design&Computer Graphics,2001,13(9):793-799.)
[3]Tan R T.Visibility in bad weather from a single image[C]//Proc of IEEE Conference on Computer Vision and Pattern Recognition.Washington DC:IEEE Computer Society,2008:1-8.
[4]He Kaiming,Sun Jian,Tang Xiaoou.Single image haze removal using dark channel prior[J].IEEE Trans on Pattern Analysis&Machine Intelligence,2011,33(12):2341-2353.
[5]He Kaiming,Sun Jian,Tang Xiaoou.Guided image filtering[J].IEEE Trans on Pattern Analysis&Machine Intelligence,2013,35(6):1397-409.
[6]Tarel J P,Hautière N.Fast visibility restoration from a single color or gray level image[C]//Proc of the 12th IEEE International Conference on Computer Vision.Piscataway,NJ:IEEE Press,2010:2201-2208.
[7]曾接贤,余永龙.双边滤波与暗通道结合的图像保边去雾算法[J].中国图象图形学报,2017,22(2):147-153.(Zeng Jiexian,Yu Yonglong.Image defogging and edge preserving algorithm based on dark channel prior and bilateral filtering[J].Journal of Image and Graphics,2017,22(2):147-153.)
[8]Narasimhan S G,Nayar S K.Contrast restoration of weather degraded images[J].IEEE Trans on Pattern Analysis&Machine Intelligence,2003,25(6):713-724.
[9]Mc Cartney E J.Optics of the atmosphere:scattering by molecules and particles[J].IEEE Journal of Quantum Electronics,1978,14(9):698-699.
[10]谢伟,周玉钦,游敏.融合梯度信息的改进引导滤波[J].中国图象图形学报,2016,21(9):1119-1126.(Xie Wei,Zhou Yuqin,You Min.Improved guided image filtering integrated with gradient information[J].Journal of Image and Graphics,2016,21(9):1119-1126.)
[11]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.
[12]蒋建国,侯天峰,齐美彬.改进的基于暗原色先验的图像去雾算法[J].电路与系统学报,2011,16(2):7-12.(Jiang Jianguo,Hou Tianfeng,Qi Meibin.Improved algorithm on image haze removal using dark channel prior[J].Journal of Circuits and Systems,2011,16(2):7-12.)
[13]孙小明,孙俊喜,赵立荣,等.暗原色先验单幅图像去雾改进算法[J].中国图象图形学报,2014,19(3):381-385.(Sun Xiaoming,Sun Junxi,Zhao Lirong,et al.Improved algorithm for single image haze removing using dark channel prior[J].Journal of Image and Graphics,2014,19(3):381-385.)
[2]朱淼良,钱徽.自然景物中大气退化模型的研究[J].计算机辅助设计与图形学学报,2001,13(9):793-799.(Zhu Miaoliang,Qian Hui.Exploring atmospheric degradation model of landscape[J].Journal of Computer-Aided Design&Computer Graphics,2001,13(9):793-799.)
[3]Tan R T.Visibility in bad weather from a single image[C]//Proc of IEEE Conference on Computer Vision and Pattern Recognition.Washington DC:IEEE Computer Society,2008:1-8.
[4]He Kaiming,Sun Jian,Tang Xiaoou.Single image haze removal using dark channel prior[J].IEEE Trans on Pattern Analysis&Machine Intelligence,2011,33(12):2341-2353.
[5]He Kaiming,Sun Jian,Tang Xiaoou.Guided image filtering[J].IEEE Trans on Pattern Analysis&Machine Intelligence,2013,35(6):1397-409.
[6]Tarel J P,Hautière N.Fast visibility restoration from a single color or gray level image[C]//Proc of the 12th IEEE International Conference on Computer Vision.Piscataway,NJ:IEEE Press,2010:2201-2208.
[7]曾接贤,余永龙.双边滤波与暗通道结合的图像保边去雾算法[J].中国图象图形学报,2017,22(2):147-153.(Zeng Jiexian,Yu Yonglong.Image defogging and edge preserving algorithm based on dark channel prior and bilateral filtering[J].Journal of Image and Graphics,2017,22(2):147-153.)
[8]Narasimhan S G,Nayar S K.Contrast restoration of weather degraded images[J].IEEE Trans on Pattern Analysis&Machine Intelligence,2003,25(6):713-724.
[9]Mc Cartney E J.Optics of the atmosphere:scattering by molecules and particles[J].IEEE Journal of Quantum Electronics,1978,14(9):698-699.
[10]谢伟,周玉钦,游敏.融合梯度信息的改进引导滤波[J].中国图象图形学报,2016,21(9):1119-1126.(Xie Wei,Zhou Yuqin,You Min.Improved guided image filtering integrated with gradient information[J].Journal of Image and Graphics,2016,21(9):1119-1126.)
[11]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.
[12]蒋建国,侯天峰,齐美彬.改进的基于暗原色先验的图像去雾算法[J].电路与系统学报,2011,16(2):7-12.(Jiang Jianguo,Hou Tianfeng,Qi Meibin.Improved algorithm on image haze removal using dark channel prior[J].Journal of Circuits and Systems,2011,16(2):7-12.)
[13]孙小明,孙俊喜,赵立荣,等.暗原色先验单幅图像去雾改进算法[J].中国图象图形学报,2014,19(3):381-385.(Sun Xiaoming,Sun Junxi,Zhao Lirong,et al.Improved algorithm for single image haze removing using dark channel prior[J].Journal of Image and Graphics,2014,19(3):381-385.)