[1]左健宏,蔺素珍,禄晓飞,等.基于雾线暗原色先验的红外图像去雾算法[J].红外技术,2020,42(6):552-558.[doi:doi:10.11846/j.issn.1001_8891.202006007]
 ZUO Jianhong,LIN Suzhen,LU Xiaofei,et al.Use of Dark Primary Color Priors for Haze-line-Based Infrared Image Dehazing[J].Infrared Technology,2020,42(6):552-558.[doi:doi:10.11846/j.issn.1001_8891.202006007]
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基于雾线暗原色先验的红外图像去雾算法
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《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
42卷
期数:
2020年第6期
页码:
552-558
栏目:
出版日期:
2020-06-23

文章信息/Info

Title:
Use of Dark Primary Color Priors for Haze-line-Based Infrared Image Dehazing
文章编号:
1001-8891(2020)06-0552-07
作者:
左健宏1蔺素珍1禄晓飞2李大威1李 毅2
1. 中北大学 大数据学院,山西 太原 030051;2. 酒泉卫星发射中心,甘肃 酒泉 735000
Author(s):
ZUO Jianhong1LIN Suzhen1LU Xiaofei2LI Dawei1LI Yi2
1. School of Big Data, North University of China, Taiyuan 030051, China;
2. Jiuquan Satellite Launch Center, Jiuquan 735000, China
关键词:
红外图像增强图像处理图像去雾全变分图像增强
Keywords:
infrared image enhancement image processing image dehazing total variation image enhancement
分类号:
TP751
DOI:
doi:10.11846/j.issn.1001_8891.202006007
文献标志码:
A
摘要:
红外图像去雾算法的主要任务是解决红外图像因米氏散射引起的低可见性和模糊。但是当前红外图像去雾算法对红外图像暗处透射率估计欠佳,针对这一情况,研究了基于雾线暗原色先验的红外图像去雾算法。首先,利用霍夫变换估计大气光照;然后,针对雾线去雾方法在部分场景中失效的现象,采用雾线暗原色先验方法,通过假设雾线较暗端为真实颜色估计透射率,获取透射率图;最后为去除透射率图中噪声,对透射率图全变分正则化进一步优化透射率图。以公开红外数据库LTIR作为测试对象,实验结果表明,本文去雾算法在增强红外图像清晰度的同时未破坏红外辐射分布,对各种场景的红外图像有较好去雾效果。透射率估计准确,有较好红外图像去雾能力。
Abstract:
The main task of infrared image dehazing algorithms is to solve the problems of low visibility and blurring in infrared images; these problems arise from Mie scattering. However, current infrared image dehazing algorithms poorly estimate the dark transmittance of infrared images. Hence, in this study, an infrared image dehazing algorithm is developed based on the dark primary color prior of the haze-line. First, the Hough transform was employed to estimate the atmospheric illumination. Second, a dark primary color prior was employed to address the failure of the haze-line dehazing method in some scenarios. The transmittance was estimated by assuming that the dark end of the haze-line corresponds to the real color, and a transmittance map was obtained. To remove noise in the transmittance map, total variation regularization was used; thus, the transmittance map was optimized. The experimental results obtained using LTIR, a public infrared dataset, as the test dataset show that the proposed algorithm can enhance the clarity of infrared images without affecting the distribution of infrared radiation; in addition, the results show that the proposed algorithm enhances infrared images corresponding to various scenes. The proposed method accurately estimates transmittance and effectively dehazes infrared images.

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备注/Memo

备注/Memo:
收稿日期:2019-11-27;修订日期:2020-01-20.

作者简介:左健宏(1995-),男,硕士研究生,主要从事图像处理,红外目标检测方面的研究。E-mail:gaoda00123@163.com。

通信作者:蔺素珍(1966-),女,教授,博士,硕士生导师,主要从事影像大数据处理,信息融合,文物虚拟修复方面的研究。E-mail:lsz@nuc.edu.cn。

基金项目:山西省应用基础研究项目(201701D121062),中北大学第十五届科技立项资助项目(2018514)。

更新日期/Last Update: 2020-06-22