基于区间估计的单幅图像快速去雾

刘海波; 杨杰; 吴正平; 张庆年; 邓勇

doi:10.11999/JEIT150403

基于区间估计的单幅图像快速去雾

doi: 10.11999/JEIT150403

2.
(武汉理工大学光纤传感与信号处理教育部重点实验室武汉 430070) ②(湖南工学院电气与信息工程学院衡阳 421002) ③(武汉理工大学交通学院武汉 430070)

基金项目:

国家自然科学基金(51479159)，交通运输部软科学项目(2013-322-811-470)

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- 被引次数: 0
出版历程
- 收稿日期: 2015-04-08
- 修回日期: 2015-10-26
- 刊出日期: 2016-02-19

Fast Single Image Dehazing Based on Interval Estimation

2.
(Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Ministry of Education, Wuhan University of Technology, Wuhan 430070, China)

Funds:

The National Natural Science Foundation of China (51479159), Soft Science Project of Chinas Ministry of Transport (2013-322-811-470)

摘要

摘要: 针对雾、霾等天气条件下捕获的图像存在严重降质现象，该文提出一种基于区间估计的单幅图像快速去雾方法。该方法从大气散射模型出发，基于暗通道先验理论，利用最小值滤波和灰度开运算，通过区间估计得到大气光值，同时得到介质传输率的初始估计值。通过对大气光照进行白平衡处理，从而得到简化大气散射模型。然后，利用简化大气散射模型和介质传输率的初始估计值，通过区间估计得到场景反照率的暗通道值，进一步得到介质传输率的粗略估计值。将介质传输率的初始估计值和粗略估计值进行像素级融合，通过联合双边滤波和值域调整得到介质传输率的最终估计值。最后，通过简化大气散射模型和色调调整得到去雾图像。实验结果表明，所提算法具有较快的运算速度，能有效提高去雾图像的清晰度和对比度，同时获得较好的色调保真度。
- 图像去雾 /
- 区间估计 /
- 暗通道先验 /
- 图像融合 /
- 双边滤波
Abstract: In order to solve the problem of degraded images captured in hazy weather, a single image dehazing method based on interval estimation is proposed. From the atmospheric scattering model, the minimal filtering and gray-scale opening operation are used to estimate the value of atmospheric light based on dark channel prior theory. At the same time, the initial estimated value of medium transmission is defined. Then, the white balance is performed and the atmospheric scattering model is simplified. Secondly, the simplified atmospheric scattering model and initial estimated value of medium transmission are used to estimate the dark channel value of scene albedo, which is adopted to obtain the coarse estimated value of medium transmission. The final estimated value of medium transmission is obtained by getting through image fusion, joint bilateral filtering and range adjustment. Finally, the simplified atmospheric scattering model and tone mapping are adopted to get the restored image. Experimental results show that the proposed algorithm has a high computation speed, effectively improves the clarity and contrast of restored image, and obtains good color fidelity.
- Image dehazing /
- Interval estimation /
- Dark channel prior /
- Image fusion /
- Bilateral filtering

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