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自适应双边滤波与方向梯度的红外图像增强

聂丰英 侯利霞 万里勇

聂丰英, 侯利霞, 万里勇. 自适应双边滤波与方向梯度的红外图像增强[J]. 红外技术, 2022, 44(12): 1309-1315.
引用本文: 聂丰英, 侯利霞, 万里勇. 自适应双边滤波与方向梯度的红外图像增强[J]. 红外技术, 2022, 44(12): 1309-1315.
NIE Fengying, HOU Lixia, WAN Liyong. Infrared Image Enhancement Based on Adaptive Bilateral Filtering and Directional Gradient[J]. Infrared Technology , 2022, 44(12): 1309-1315.
Citation: NIE Fengying, HOU Lixia, WAN Liyong. Infrared Image Enhancement Based on Adaptive Bilateral Filtering and Directional Gradient[J]. Infrared Technology , 2022, 44(12): 1309-1315.

自适应双边滤波与方向梯度的红外图像增强

基金项目: 

2021年江西省教育厅科学技术研究项目 GJJ212517

2021年教育部产学合作协同育人项目 202102450009

2020年省教育厅科技技术项目 GJJ202506

江西省教育科学“十三五”规划2019年度课题 19YB266

南昌工学院机器人与智能系统研究中心项目 NGYZY-20-005

详细信息
    作者简介:

    聂丰英(1982-),女,江西丰城人,硕士,副教授,研究方向为图像处理与数据库技术

    通讯作者:

    万里勇(1976-),男,江西东乡人,博士,教授,研究方向为图像处理、移动边缘计算和智能信息处理,E-mail:wanliyong@163.com

  • 中图分类号: TP391

Infrared Image Enhancement Based on Adaptive Bilateral Filtering and Directional Gradient

  • 摘要: 为克服现有的红外图像增强方法存在欠增强、过增强以及对比度不高等缺陷,提出了自适应双边滤波与方向梯度的红外图像增强方法。对双边滤波进行改进,加权系数自适应于平滑区域和细节区域,以作为Retinex的中心环绕函数,将红外图像分解为基础层和细节层;用改进的平台直方图均衡化对基础层图像进行增强;提出一种方向梯度算子,用其提取细节层图像的梯度图,进而对细节层图像进行非线性的自适应边缘增强。实验结果表明,相对于部分现有的方法,本文方法能更有效地提升红外图像的亮度和对比度,增强图像的视觉效果更佳。
  • 图  1  标准双边滤波与改进的双边滤波的效果

    Figure  1.  Effect of bilateral filtering and improved bilateral filtering

    图  2  正态分布的3σ准则

    Figure  2.  3σ criterion of normal distribution

    图  3  多向梯度算子

    Figure  3.  Multi-directional gradient operator

    图  4  场景一各算法的增强效果

    Figure  4.  Enhancement effect of each algorithm for scenario 1

    图  5  场景二各算法的增强效果

    Figure  5.  Enhancement effect of each algorithm for scenario 2

    图  6  场景三各算法的增强效果

    Figure  6.  Enhancement effect of each algorithm for scenario 3

    图  7  改进的双边滤波对三个场景的增强效果

    Figure  7.  Enhancement effect of improved bilateral filtering on three scenarios

    表  1  各算法对三个场景图像增强的信息熵

    Table  1.   Information entropy of each algorithm for image enhancement of three scenes

    Methods Original image Ref. [7] Ref. [11] Ref. [12] Ref. [13] Proposed method
    Scenario 1 6.79 7.05 6.83 6.85 6.92 7.11
    Scenario 2 6.85 7.12 6.96 6.98 7.12 7.26
    Scenario 3 6.45 6.75 6.53 6.64 6.59 6.92
    下载: 导出CSV
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    WEI R, ZHAO R, XU X, et al. Infrared image detail enhancement based on histogram[J]. Infrared Technology, 2016, 38(6): 472-475. http://hwjs.nvir.cn/article/id/hwjs201606005
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    [8] 吕侃徽, 张大兴. 基于自适应直方图均衡化耦合拉普拉斯变换的红外图像增强算法[J]. 光学技术, 2021, 47(6): 747-753. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJS202106018.htm

    LV K, ZHANG D. Infrared image enhancement algorithm based on adaptive histogram equalization coupled with Laplace transform[J]. Optical Technique, 2021, 47(6): 747-753. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJS202106018.htm
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    WEI R, ZHAN W, ZHU D, et al. Improved multi-scale Retinex infrared image enhancement[J]. Chinese Journal of Liquid Crystals and Displays, 2021, 36(3): 465-474. https://www.cnki.com.cn/Article/CJFDTOTAL-YJYS202103014.htm
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    FAN Z, BI D, MA, et al. Method for infrared image with brightness preservation and detail enhancement[J]. Journal of Central South University (Science and Technology), 2016, 47(6): 1967-1972. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201606020.htm
    [16] LI S, JIN W, LI L, et al. An improved contrast enhancement algorithm for infrared images based on adaptive double plateaus histogram equalization[J]. Infrared Physics & Technology, 2018, 90: 164-174.
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出版历程
  • 收稿日期:  2022-01-25
  • 修回日期:  2022-03-22
  • 刊出日期:  2022-12-20

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