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基于改进暗通道和导向滤波的单幅图像去雾算法

陈书贞 任占广 练秋生

陈书贞, 任占广, 练秋生. 基于改进暗通道和导向滤波的单幅图像去雾算法. 自动化学报, 2016, 42(3): 455-465. doi: 10.16383/j.aas.2016.c150212
引用本文: 陈书贞, 任占广, 练秋生. 基于改进暗通道和导向滤波的单幅图像去雾算法. 自动化学报, 2016, 42(3): 455-465. doi: 10.16383/j.aas.2016.c150212
CHEN Shu-Zhen, REN Zhan-Guang, LIAN Qiu-Sheng. Single Image Dehazing Algorithm Based on Improved Dark Channel Prior and Guided Filter. ACTA AUTOMATICA SINICA, 2016, 42(3): 455-465. doi: 10.16383/j.aas.2016.c150212
Citation: CHEN Shu-Zhen, REN Zhan-Guang, LIAN Qiu-Sheng. Single Image Dehazing Algorithm Based on Improved Dark Channel Prior and Guided Filter. ACTA AUTOMATICA SINICA, 2016, 42(3): 455-465. doi: 10.16383/j.aas.2016.c150212

基于改进暗通道和导向滤波的单幅图像去雾算法

doi: 10.16383/j.aas.2016.c150212
基金项目: 

河北省自然科学基金 F2014203076

国家自然科学基金 61471313

详细信息
    作者简介:

    陈书贞 燕山大学信息科学与工程学院副教授.主要研究方向为图像处理, 压缩感知及生物识别.E-mail:chen_sz818@163.com

    任占广 燕山大学信息科学与工程学院硕士研究生.主要研究方向为图像处理和图像去雾.E-mail:renzg13@163.com

Single Image Dehazing Algorithm Based on Improved Dark Channel Prior and Guided Filter

Funds: 

Natural Science Foundation of Hebei Province F2014203076

National Natural Science Foundation of China 61471313

More Information
    Author Bio:

    Associate professor at the School of Information Science and Engineering, Yanshan University. Her research interest covers image processing, compressed sensing, and biometrics recognition

    Master student at the School of Information Science and Engineering, Yanshan University. His research interest covers image processing and image haze removal

    Corresponding author: LIAN Qiu-Sheng Professor at the School of Information Science and Engineering, Yanshan University. His research interest covers image processing, sparse representation, compressed sensing, and multi-scale geometrical analysis. Corresponding author of this paper
  • 摘要: 针对单幅雾霾图像中包含的大面积天空或白色物体等区域暗通道先验失效和导向滤波去雾方法去雾不彻底的问题, 提出了一种基于改进暗通道和导向滤波的单幅图像去雾算法.首先基于暗通道引入了混合暗通道, 然后对混合暗通道进行映射处理, 从而得到大气耗散函数粗估计值; 利用导向滤波方法优化大气耗散函数粗估计值, 进而求解环境光值和初始传输图; 利用全变差正则化方法对初始传输图进行优化, 以解决其平滑性较差的问题.实验结果表明, 本文算法得到的去雾图像具有较高的清晰度, 对于大面积天空或白色物体区域也能实现良好的去雾效果.
  • 图  1  单幅图像去雾

    Fig.  1  Single image dehazing

    图  2  混合暗通道改进前后去雾效果对比结果

    Fig.  2  The comparative results of improved algorithm and unimproved mixed dark channel

    图  3  阈值(T, L)对去雾结果的影响

    Fig.  3  The influence of thresholds (T, L) on recovered images

    图  4  全变差优化前后去雾效果对比结果

    Fig.  4  The comparative results of improved algorithm and unimproved total variation filter

    图  5  本文算法与He的算法去雾效果对比

    Fig.  5  Comparison with He0s work

    图  6  普通浓雾霾图像去雾效果对比

    Fig.  6  Comparison with others0 work in ordinary hazy images

    图  7  包含大面积天空雾霾图像去雾效果对比

    Fig.  7  Comparison with others0 work in hazy images with large sky regions

    图  8  本文算法与Tang的算法去雾效果对比

    Fig.  8  Comparison with Tang0s work

    图  9  本文算法与Wang的算法去雾效果对比

    Fig.  9  Comparison with Wang0s work

    图  10  不同算法去雾结果比较

    Fig.  10  Comparison with others0 work

    表  1  图 6中去雾时间对比

    Table  1  Comparison of time consumed in Fig. 6

    图像图像尺寸He0s(s)Ours(s)
    南瓜600×40022.822.64
    风景600×52529.674.03
    下载: 导出CSV
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出版历程
  • 收稿日期:  2015-04-20
  • 录用日期:  2015-11-02
  • 刊出日期:  2016-03-01

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