结合改进Retinex及自适应分数阶微分的雾霾公路交通图像增强

王卫星; 赵恒

doi:10.3788/OPE.20202808.1820

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结合改进Retinex及自适应分数阶微分的雾霾公路交通图像增强

信息科学 | 更新时间：2020-08-14

- 结合改进Retinex及自适应分数阶微分的雾霾公路交通图像增强
- Haze traffic image enhancement based on improved retinex and adaptive fractional differential
- 光学精密工程 2020年28卷第8期页码：1820-1834
- 作者机构：
  
  长安大学信息工程学院, 陕西西安 710064
- 作者简介：
  
  [ "王卫星(1959-), 男, 湖南人, 博士, 教授, 博士生导师, 1982年和1985年分别在国内获得学士学位和工程硕士, 1997年在瑞典皇家工学院获得博士学位, 主要从事图像处理、模式识别和机器视觉等研究方向。E-mail:znn525d@qq.com" ]
  [ "赵恒(1993-), 男, 陕西人, 硕士研究生, 2016年于西安石油大学获得学士学位, 主要从事数字图像处理及计算机视觉的研究。E-mail:271636188@qq.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(61170147);国家自然科学基金资助项目(1972060)
- DOI：10.3788/OPE.20202808.1820
  中图分类号： TP391.4
- 收稿日期：2020-03-24，
  
  修回日期：2020-04-15，
  
  录用日期：2020-4-15，
  
  纸质出版日期：2020-08-25
- 稿件说明：
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王卫星, 赵恒. 结合改进Retinex及自适应分数阶微分的雾霾公路交通图像增强[J]. 光学精密工程, 2020,28(8):1820-1834.

Wei-xing WANG, Heng ZHAO. Haze traffic image enhancement based on improved retinex and adaptive fractional differential[J]. Optics and precision engineering, 2020, 28(8): 1820-1834.
王卫星, 赵恒. 结合改进Retinex及自适应分数阶微分的雾霾公路交通图像增强[J]. 光学精密工程, 2020,28(8):1820-1834. DOI： 10.3788/OPE.20202808.1820.

Wei-xing WANG, Heng ZHAO. Haze traffic image enhancement based on improved retinex and adaptive fractional differential[J]. Optics and precision engineering, 2020, 28(8): 1820-1834. DOI： 10.3788/OPE.20202808.1820.

摘要

为了提高雾霾天气条件下交通图像的对比度，清晰度和颜色保真度，减少图像退化所带来的负面影响，提出了一种采用快速引导滤波平滑约束的Retinex及自适应分数阶微分的雾霾天气交通图像增强算法。首先，该方法将原图像从RGB转换到YCbCr颜色空间，提取亮度分量构建初始图像；其次，构建变分模型，借助快速引导滤波构造目标函数的平滑约束项来准确估计初始照射分量；然后，使用Retinex模型获得初始反射分量，再采用自适应分数阶微分掩膜对初始反射分量进行增强得到亮度分量的增强结果，该方法在图像噪声抑制和细节增强方面性能良好；最后，将处理后的反射分量结合

，

色差信息从YCbCr转换到RGB颜色空间即得到最终增强图像。本文对不同的雾霾交通图像进行了对比实验，实验结果表明，新方法的标准差（STD）和平均梯度（AG）较原图至少提高1.12倍和4倍以上，信息熵（E）至少提高4.76%以上，综合性能优于其他的对比算法。新方法在图像增强和细节保持方面得到了很好地改进，有效地提高了雾霾天气条件下公路交通图像的颜色保真度、对比度和细节清晰度等，使得增强后的图像视觉效果和可视度明显改善，更加真实自然。

Abstract

To improve the contrast

clarity

and color fidelity of the traffic image in haze weather and reduce the negative impact of image degradation

a haze traffic image enhancement algorithm based on fast-guided filtering smoothing constraint for Retinex and adaptive fractional differential was proposed herein. First

an original image was converted from RGB space to YCbCr space

and the brightness component

was extracted to construct the initial image. Second

a variational model was constructed

and the fast-guided filter was used to construct the smoothing constraints of the objective function to accurately estimate the initial irradiation component. Then

the Retinex model was used to obtain the initial reflection component

and the adaptive fractional differential mask was applied to enhance in order to obtain

which was the enhancement result of the

component. The method showed good performance in terms of image noise suppression and detail enhancement. Finally

the reflection component was converted from YCbCr to RGB space by combining with the color difference information of Cb and Cr

to obtain the final enhanced image. In this study

the contrast experiments of different haze traffic images were tested. The experimental results indicate that the standard deviationand average gradient of the new method are at least 1.12 times and 4 times higher than those of the original image

and the information entropyis at least 4.76% higher.The comprehensive performance of the proposed method is better than that of other comparison algorithms. Thus

the method is satisfactory for image enhancement and detail retention.It effectively improves the color fidelity

intensity contrast

and texture clarity forroad traffic images in haze weather.Moreover

it makes the image more visible

and the color more natural.

关键词

Keywords

references

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