改进的暗原色先验理论视频去雾算法研究
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摘要
针对暗原色先验算法出现的边缘残雾、天空区域彩色失真、去雾后图像偏暗以及实时性差等问题,提出了一种基于点暗原色先验和引导滤波的视频去雾算法。采用逐点式最小值滤波来消除块效应,并利用四叉树法来快速准确地估计大气光值,结合直方图均衡化技术来增强图像,改善视觉效果,同时利用图像采样技术和引导滤波优化算法提高速度。实验结果显示,该算法的去雾图像清晰,运算量小,适用范围广,鲁棒性好,适合实时视频去雾。
Dark channel prior often keeps residual haze near depth edges after haze removal,and leads to a dim image and color distortion in sky area.Moreover,its processing speed is too slow.To solve these problems,this paper proposes an effective and fast method for real-time video dehazing based on dark channel prior and guided filter.Firstly,this paper replaces patch-based dark channel prior into pixel-based dark channel prior to eliminate block effect,applies guided filter to refine the transmission map,and employs quad-tree subdivision to estimate atmospheric light precisely.Furthermore,in order to get a visually-pleasing result,this paper adopts histogram equalization to enhance dehazed image.Finally,this paper uses downsampling and redundant information between each frame to reduce computational burden.Experimental results show that the proposed method is much better,it has less calculation quantity and a wide range of application suitable for real-time video dehazing.
Dark channel prior often keeps residual haze near depth edges after haze removal,and leads to a dim image and color distortion in sky area.Moreover,its processing speed is too slow.To solve these problems,this paper proposes an effective and fast method for real-time video dehazing based on dark channel prior and guided filter.Firstly,this paper replaces patch-based dark channel prior into pixel-based dark channel prior to eliminate block effect,applies guided filter to refine the transmission map,and employs quad-tree subdivision to estimate atmospheric light precisely.Furthermore,in order to get a visually-pleasing result,this paper adopts histogram equalization to enhance dehazed image.Finally,this paper uses downsampling and redundant information between each frame to reduce computational burden.Experimental results show that the proposed method is much better,it has less calculation quantity and a wide range of application suitable for real-time video dehazing.
引文
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[11]杨万挺,汪荣贵,方帅,等.滤波器可变的Retinex雾天图像增强算法[J].计算机辅助设计与图形学学报,2010(6):965-971.
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[13]郭璠,蔡自兴.图像去雾算法清晰化效果客观评价方法[J].自动化学报,2012(9):1410-1419.
[14]李大鹏,禹晶,肖创柏.图像去雾的无参考客观质量评测方法[J].中国图象图形学报,2011(9):1753-1757.
[15]刘海波,汤群芳,杨杰.去雾图像质量客观评价方法研究[J].量子电子学报,2016(1):20-28.
[2]吴迪,朱青松.图像去雾的最新研究进展[J].自动化学报,2015(2):221-239.
[3]曾浩,尚媛园,丁辉,等.基于暗原色先验的图像快速去雾[J].中国图象图形学报,2015(7):914-921.
[4]龙伟,傅继贤,李炎炎,等.基于大气消光系数和引导滤波的浓雾图像去雾算法[J].四川大学学报:工程科学版,2016(4):175-180.
[5]傅继贤,龙伟,李炎炎.对行车视程估值的车前图像去雾算法[J].科学技术与工程,2016(27):209-213.
[6]王萍,张春,罗颖昕.一种雾天图像低对比度增强的快速算法[J].计算机应用,2006(1):152-153.
[7]刘茜,卢心红,李象霖.基于多尺度Retinex的自适应图像增强方法[J].计算机应用,2009(8):2077-2079.
[8]He K M,Sun J A,Tang X O.Single image haze removal using dark channel prior[C]//IEEE Conference on Computer Vision and Pattern Recognition,2009:1956-1963.
[9]Narasimhan S G,Nayar S K.Vision and the atmosphere[J].International Journal of Computer Vision,2002,48(3):233-254.
[10]He K M,Sun J A,Tang X O.Guided image filtering[C]//LNCS 6311:European Conference on Computer Vision.Berlin:Springer-Verlag,2010:1-14.
[11]杨万挺,汪荣贵,方帅,等.滤波器可变的Retinex雾天图像增强算法[J].计算机辅助设计与图形学学报,2010(6):965-971.
[12]杨国庆,崔荣一.基于纹理特征的自然图像鉴别方法[J].计算机应用研究,2010(7):2783-2785.
[13]郭璠,蔡自兴.图像去雾算法清晰化效果客观评价方法[J].自动化学报,2012(9):1410-1419.
[14]李大鹏,禹晶,肖创柏.图像去雾的无参考客观质量评测方法[J].中国图象图形学报,2011(9):1753-1757.
[15]刘海波,汤群芳,杨杰.去雾图像质量客观评价方法研究[J].量子电子学报,2016(1):20-28.