基于改进型Retinex算法的雾天图像增强技术
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摘要
为增强雾天图像的对比度及颜色和亮度恒常性,提出了一种改进型Retinex算法雾天图像增强算法。使用改进的双边滤波器作为滤波函数,在保持边缘信息的同时去除噪声的干扰;并使用S型函数曲线对Retinex算法中对数域相减去除入射光分量的图像进行颜色恢复处理,增强整幅图像的对比度和感知特性,还原图像的色彩信息。实验结果表明,所提的改进算法能有效提高雾天图像的清晰度和对比度,相较原雾天图像清晰度提升约200%,标准差提升约110%,信息熵提升约10%。同时,可保持更加真实鲜艳的图像颜色,计算复杂度较低,满足实时性要求。
In order to enhance the contrast,color and brightness constancy of foggy images,an improved Retinex algorithm of foggy image enhancement is proposed.The algorithm uses an improved bilateral filter as a filter function to remove the noise interference while maintaining the edge information.It uses the S-shape function curve to restore image color by subtracting the incident light component from the logarithmic domain in Retinex algorithm,enhances the contrast and the perceptual characteristics of the whole image,and restores the color information of the image.The experimental results show that the improved algorithm proposed in this paper can effectively improve the clarity and contrast of the foggy image.Compared with the original foggy image,the image clarity is raised by about 200%,the standard deviation is raised by about 110%,and the information entropy is raised by about 10%.At the same time,it can maintain more realistic color information,and the computational complexity is low,which meets the real-time requirements.
In order to enhance the contrast,color and brightness constancy of foggy images,an improved Retinex algorithm of foggy image enhancement is proposed.The algorithm uses an improved bilateral filter as a filter function to remove the noise interference while maintaining the edge information.It uses the S-shape function curve to restore image color by subtracting the incident light component from the logarithmic domain in Retinex algorithm,enhances the contrast and the perceptual characteristics of the whole image,and restores the color information of the image.The experimental results show that the improved algorithm proposed in this paper can effectively improve the clarity and contrast of the foggy image.Compared with the original foggy image,the image clarity is raised by about 200%,the standard deviation is raised by about 110%,and the information entropy is raised by about 10%.At the same time,it can maintain more realistic color information,and the computational complexity is low,which meets the real-time requirements.
引文
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[15]刘晓阳,乔通,乔智.基于双边滤波和Retinex算法的矿井图像增强方法[J].工矿自动化,2017,43(2):49-54.LIU X Y,QIAO T,QIAO Z.Image enhancement method of mine based on bilateral filtering and Retinex algorithm[J].Industry and Mine Automation,2017,43(2):49-54(in Chinese).
[16]LIU C,CHENG I,ZHANG Y,et al.Enhancement of low visibility aerial images using histogram truncation and an explicit Retinex representation for balancing contrast and color consistency[J].ISPRS Journal of Photogrammetry&Remote Sensing,2017,128:16-26.
[17]MA J,FAN X,NI J,et al.Multi-scale Retinex with color restoration image enhancement based on Gaussian filtering and guided filtering[J].International Journal of Modern Physics B,2017,31(16-19):1744077.
[18]YU T H,MENG X,ZHU M,et al.An improved multi-scale Retinex FOG and haze image enhancement method[C]∥International Conference on Information System and Artificial Intelligence.Piscataway,NJ:IEEE Press,2017:557-560.
[2]刘春辉,齐越,丁文锐.基于大气光鲁棒估计的无人机图像去雾方法[J].北京航空航天大学学报,2017,43(6):1105-1111.LIU C H,QI Y,DING W R.A haze removal method for unmanned aerial vehicle images based on robust estimation of atmospheric light[J].Journal of Beijing University of Aeronautics and Astronautics,2017,43(6):1105-1111(in Chinese).
[3]王浩,张叶,沈宏海,等.图像增强算法综述[J].中国光学,2017,10(4):438-448.WANG H,ZHANG Y,SHEN H H,et al.Review of image enhancement algorithms[J].Chinese Optics,2017,10(4):438-448(in Chinese).
[4]HE K,SUN J,TANG X.Single image haze removal using dark channel prior[C]∥IEEE Conference on Computer Vision and Pattern Recognition,2009.Piscataway,NJ:IEEE Press,2009:1956-1963.
[5]赵春丽,董静薇.基于暗通道及多尺度Retinex的雾霾天气图像增强算法[J].激光杂志,2018,39(1):104-109.ZHAO C L,DONG J W.Image enhancement algorithm of haze weather based on dark channel and multi-scale Retinex[J].Laser Journal,2018,39(1):104-109(in Chinese).
[6]刘海波,杨杰,吴正平,等.基于暗通道先验和Retinex理论的快速单幅图像去雾方法[J].自动化学报,2015,41(7):1264-1273.LIU H B,YANG J,WU Z P,et al.A fast single image dehazing method based on dark channel prior and Retinex theory[J].Acta Automatica Sinica,2015,41(7):1264-1273(in Chinese).
[7]ZHANG W,LIANG J,JU H,et al.Study of visibility enhancement of hazy images based on dark channel prior in polarimetric imaging[J].Optik-International Journal for Light and Electron Optics,2017,130:123-130.
[8]JI W,QIAN Z,XU B,et al.A nighttime image enhancement method based on Retinex and guided filter for object recognition of apple harvesting robot[J].International Journal of Advanced Robotic Systems,2018,15(1):1-12.
[9]WAN M,GU G,QIAN W,et al.Infrared small target enhancement:Grey level mapping based on improved sigmoid transformation and saliency histogram[J].Journal of Modern Optics,2018,65(10):1161-1179.
[10]黄宇晴,丁文锐,李红光.基于图像增强的无人机侦察图像去雾方法[J].北京航空航天大学学报,2017,43(3):592-601.HUANG Y Q,DING W R,LI H G.Haze removal method for UAV reconnaissance images based on image enhancement[J].Journal of Beijing University of Aeronautics and Astronautics,2017,43(3):592-601(in Chinese).
[11]高明,秦世引.基于非线性直方图变换的对比度畸变图像校正[J].北京航空航天大学学报,2016,42(3):514-521.GAO M,QIN S Y.Correction of contrast distortion image based on nonlinear transform of histogram[J].Journal of Beijing University of Aeronautics and Astronautics,2016,42(3):514-521(in Chinese).
[12]谢娜.基于图像增强的图像去雾算法研究[J].机械设计与制造工程,2017,46(12):31-33.XIE N.Research on the image defogging algorithm based on image enhancement[J].Machine Design and Manufacturing Engineering,2017,46(12):31-33(in Chinese).
[13]PARK S,YU S,MOON B,et al.Low-light image enhancement using variational optimization-based Retinex model[J].IEEETransactions on Consumer Electronics,2017,63(2):178-184.
[14]XU K,JUNG C.Retinex-based perceptual contrast enhancement in images using luminance adaptation[C]∥IEEE International Conference on Acoustics,Speech and Signal Processing.Piscataway,NJ:IEEE Press,2017:1363-1367.
[15]刘晓阳,乔通,乔智.基于双边滤波和Retinex算法的矿井图像增强方法[J].工矿自动化,2017,43(2):49-54.LIU X Y,QIAO T,QIAO Z.Image enhancement method of mine based on bilateral filtering and Retinex algorithm[J].Industry and Mine Automation,2017,43(2):49-54(in Chinese).
[16]LIU C,CHENG I,ZHANG Y,et al.Enhancement of low visibility aerial images using histogram truncation and an explicit Retinex representation for balancing contrast and color consistency[J].ISPRS Journal of Photogrammetry&Remote Sensing,2017,128:16-26.
[17]MA J,FAN X,NI J,et al.Multi-scale Retinex with color restoration image enhancement based on Gaussian filtering and guided filtering[J].International Journal of Modern Physics B,2017,31(16-19):1744077.
[18]YU T H,MENG X,ZHU M,et al.An improved multi-scale Retinex FOG and haze image enhancement method[C]∥International Conference on Information System and Artificial Intelligence.Piscataway,NJ:IEEE Press,2017:557-560.