计算机应用 ›› 2014, Vol. 34 ›› Issue (6): 1753-1757.DOI: 10.11772/j.issn.1001-9081.2014.06.1753

• 虚拟现实与数字媒体 • 上一篇    下一篇

基于人眼视觉特性的快速单幅图像去雾算法

张红英1,张赛楠2,吴亚东2,吴斌1   

  1. 1. 西南科技大学 信息工程学院, 四川 绵阳 621010;
    2. 西南科技大学 计算机科学与技术学院,四川 绵阳 621010
  • 收稿日期:2013-11-05 修回日期:2013-12-30 出版日期:2014-06-01 发布日期:2014-07-02
  • 通讯作者: 张红英
  • 作者简介:张红英(1976-),女,四川德阳人,教授,博士,主要研究方向:图像分析与处理、运动目标检测与跟踪;张赛楠(1986-),女,河南周口人,助教,硕士,主要研究方向:图像增强、可视化;吴亚东(1979-),男,河南舞阳人,教授,博士,主要研究方向:图像处理、可视化;吴斌(1965-),男,四川大竹人,教授,博士,主要研究方向:最经济智能控制、图像处理。
  • 基金资助:

    四川省科技厅青年基金资助项目;中国科学院西部之光人才培养计划

Fast haze removal algorithm for single image based on human visual characteristics

ZHANG Hongying1,ZHANG Sainan2,WU Yadong2,WU Bin1   

  1. 1. School of Information Engineering, Southwest University of Science and Technology, Mianyang Sichuan 621010, China;
    2. School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang Sichuan 621010, China
  • Received:2013-11-05 Revised:2013-12-30 Online:2014-06-01 Published:2014-07-02
  • Contact: ZHANG Hongying

摘要:

为从降质图像中去除天气的影响,提出一种快速的图像去雾算法。该算法基于二色大气散射模型,针对雾天图像的亮度分布特点以及人眼的视觉特性,首先采用亮度分量来估计粗略传输图,然后采用线性空域滤波对粗略传输图进行细化处理,并利用大气散射模型得到复原图像,最后采用基于人眼视觉特性的拟合函数对复原图像进行亮度调节,使恢复的图像更自然、清晰。大量实验结果表明,该算法恢复的图像在对比度、信息熵和运算时间等客观评价标准上都优于现存算法,具有良好的视觉效果。

Abstract:

In order to remove the effect of weather in degraded image, a fast haze removal algorithm for single image based on human visual characteristics was proposed. According to the luminance distribution of the hazy image and the human visual characteristics, the proposed method first applied luminance component to estimate coarse transmission map, then used a linear spatial filter to refine the transmission map and obtained the dehazed image by the atmospheric scattering model. Finally a new image enhancement fitting function was applied to enhance the luminance component of the dehazed image to make it more natural and clear. The experimental results show that the proposed algorithm effectively removes haze and is better than the existing algorithms in terms of contrast, information entropy and computing time.

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