Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (4): 1169-1181.doi: 10.13229/j.cnki.jdxbgxb20200382

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Research progress of image dehazing algorithms

Hua-wei JIANG1,2(),Zhen YANG1,2,3,Xin ZHANG2,Qian-lin DONG1,2   

  1. 1.Key Laboratory of Grain Information Processing and Control Ministry of Education，Henan University of Technology，Zhengzhou 450001，China
    2.College of Information Science and Engineering，Henan University of Technology，Zhengzhou 450001，China
    3.State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines，Anhui University of Science and Technology，Huainan 232001，China
  • Received:2020-05-30 Online:2021-07-01 Published:2021-07-14

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Abstract:

According to the algorithm processing method, image dehazing technology can be divided into two categories. One is image enhancement algorithm, which involves histogram equalization, wavelet transform and Retinex algorithm based on color constancy theory. The other is the image restoration and defogging algorithm, which mainly includes the traditional multi-image restoration based on the characteristics of optical polarization and the single image restoration based on a priori assumption, and the emerging image restoration algorithm based on deep learning. In order to better study the image dehazing algorithm in the future, the main development process of image dehazing techniques was reviewed in this paper, the existing problems for the study on image dehazing algorithm was analyzed, and the development trend was tried to explore.

Key words: information processing technology, fog-degraded image, image dehazing, image enhancement, image restoration

CLC Number: 

  • TP391.4

Fig.1

Comparison of different image histograms"

Fig.2

Result comparison of histogram equalization algorithm"

Fig.3

Comparison of SSR and MSR results"

Fig.4

Processing results of image restoration method based on dark channel prior"

Fig.5

Results of typical image defogging algorithm"

Table 1

Objective evaluation of typical image defogging algorithms"

图像去雾算法运算时间/serˉσ
全局直方图均衡化0.002 40.153 432.7390.211 32
局部直方图均衡化0.024 20.192 853.6540.200 98
单尺度Retinex0.044 30.227 375.2560.219 67
多尺度Retinex0.103 90.345 973.6610.194 03
暗通道先验算法0.135 80.411 582.8250.127 57
中值滤波法0.072 40.398 813.0880.199 25
DehazeNet0.891 30.415 392.4610.115 39
DANet0.338 10.399 262.5230.105 93

Table 2

Comparison of typical image defogging algorithms"

图像去雾算法优点缺点适用情况参考来源
全局直方图均衡化算法简单、运算时间短、图像灰度离散程度高合并较多灰度级，细节信息丢失，不适合景深多变的图像单一景深图像Jain［57］，1989
局部直方图均衡化整体对比度较强，能够保留图像细节信息运算量大，存在局部块状效应静态浓雾图像

Kim［58］，1997；

江巨浪等［10］，2006

小波变换可去除光照不均产生的黑斑，能较好地保持图像的原貌占用较大的运算空间薄雾、水下图像Mallat［59］，1989；吴颖谦［14］，2003
局部单尺度Retinex（SSR）弥补边缘曲线增强的局限性产生光晕现象，整体亮度不足薄雾图像Jobson等［19］，1997
局部多尺度Retinex（MSR）相比SSR，图像处理后亮度得到提升色彩饱和度不够，有轻度失真环境亮度均匀的图像汪荣贵等［24］，2010
带色彩恢复的多尺度Retinex（MSRCR）性能比较稳定，细节信息完善算法复杂度增加，运算时间长色彩失真的静态图像Rahman等［60］，1997
基于不同偏振度的多幅图像复原能够消除偏振光的影响依赖环境光的偏振状态和偏振度薄雾图像，光线较好的图像Schechner等［30］，2001；Namer等［31］，2005
暗通道先验能够较大限度地保留图像细节信息对大气光亮度估计较差，易造成细节缺失景深连续且无遮挡的图像He等［35］，2011
中值滤波法时间复杂度低，去雾性能高物体边缘易产生块状效应主体边缘简洁明确的图像胡研等［61］，2018
端到端去雾DehazeNet利用神经网络高效估计退化模型参数经特征提取等步骤，运算速度有限制无杂乱纹理的自然场景图像Cai等［46］，2016
DANet基于合成域和真实域，高效恢复雾图像算法复杂度相对于其他神经网络算法较高非极端环境的图像Shao等［55］，2020
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