基于改进暗通道算法的红外图像去雾

王昊昱; 何明枢

基于改进暗通道算法的红外图像去雾

王昊昱^1,,
何明枢^2, ,

1.
北京邮电大学现代邮政学院, 北京 102206
2.
北京邮电大学网络空间安全学院, 北京 100876

基金项目:

国家重点研究计划课题 2018YFC0831501

详细信息

作者简介:
王昊昱（2001-），女，本科，北京市人，主要研究方向：信息处理及系统设计。E-mail: whaoyu3@126.com

通讯作者:
何明枢（1995-），男，博士，甘肃兰州人，主要研究方向：人工智能与大数据。E-mail: hemingshu@bupt.edu.cn

中图分类号: TP391.4
计量
- 文章访问数: 175
- HTML全文浏览量: 319
- PDF下载量: 83
- 被引次数: 0
出版历程
- 收稿日期: 2022-04-21
- 修回日期: 2022-05-05
- 刊出日期: 2022-08-20

Infrared Image Dehazing Based on Improved Dark Channel Prior

WANG Haoyu^1
,,
HE Mingshu^{2
, ,}

1.
School of Modern Post, Beijing University of Posts and Telecommunications, Beijing 102206, China
2.
School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China

摘要

摘要: 为了提高红外图像去雾效果，提出改进暗通道算法。首先利用近红外光在天空区域与非天空区域的穿透能力不同，天空区域的红外能量相对非天空区域能量较小，通过红外有雾图像的能量差异性划分为天空区域、非天空区域；接着天空区域的大气光值通过滑动窗口的像素亮度平均值计算，透射率考虑近红外波段衰减，非天空区域的大气光值、透射率通过改进暗通道算法计算；最后通过各区域大气光值、透射率恢复出无雾图像。实验结果表明，本文算法对红外图像去雾结果清晰，图像细节信息较好，评价指标较优。
- 能量差异 /
- 天空区域 /
- 滑动窗口 /
- 红外图像去雾
Abstract: To improve the effectiveness of infrared image dehazing, an improved dark channel was proposed. First, because the penetration ability of near-infrared light in the sky region is different from that in the non-sky region, and the infrared energy in the sky region is smaller than that in the non-sky region, the region was divided into sky and non-sky regions using the energy difference. Second, the atmospheric light value of the sky region was calculated using the average pixel brightness of the sliding window, the near-infrared wave attenuation was considered for the transmittance, and the atmospheric light value and transmittance of the non-sky region were calculated using the improved dark channel algorithm. Finally, the dehazed image was recovered from the atmospheric light value and transmittance of each region. The experimental results show that the dehazing output of the infrared image was clearer, the image detail information was better, and the evaluation index was better than those of other algorithms.
- energy difference /
- sky region /
- sliding window /
- infrared image dehazing

HTML全文

图 1 t_sky(x)优化对比

Figure 1. t_sky(x) optimization comparison

下载: 全尺寸图片幻灯片

图 2 v与w关系

Figure 2. Relationship between v and w

下载: 全尺寸图片幻灯片

图 3 算法对比分析

Figure 3. Comparative analysis of algorithms

下载: 全尺寸图片幻灯片

图 4 各种算法对红外图像去雾对比效果（场景1）

Figure 4. Comparison of infrared image dehazing results after using various algorithms(Scene 1)

下载: 全尺寸图片幻灯片

图 5 各种算法对红外图像去雾对比效果（场景2）

Figure 5. Comparison of infrared image dehazing results after using various algorithms(Scene 2)

下载: 全尺寸图片幻灯片

图 6 评价指标分析

Figure 6. Evaluation index analysis

下载: 全尺寸图片幻灯片

表 1 天空区域灰度值与h(x)取最小值关系

Table 1. Relationship between gray value of sky area and minimum value of h(x)

Gray value of sky area h(x)

≤160 ≥1

161-170 ≥1.1

171-180 ≥1.6

181-190 ≥5

191-200 ≥10

201-210 ≥20

211-220 ≥35

221-230 ≥55

231-240 ≥80

241-255 ≥140

下载: 导出CSV

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