WANG Haoyu, HE Mingshu. Infrared Image Dehazing Based on Improved Dark Channel Prior[J]. Infrared Technology , 2022, 44(8): 875-881.
Citation: WANG Haoyu, HE Mingshu. Infrared Image Dehazing Based on Improved Dark Channel Prior[J]. Infrared Technology , 2022, 44(8): 875-881.

Infrared Image Dehazing Based on Improved Dark Channel Prior

More Information
  • Received Date: April 20, 2022
  • Revised Date: May 04, 2022
  • 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.
  • [1]
    张健. 基于改进暗通道算法的红外图像去雾研究[J]. 激光与红外, 2021, 51(8): 1081-1087. DOI: 10.3969/j.issn.1001-5078.2021.08.018

    ZHANG Jian. Infrared image dehazing based on improveddark channel prior algorithm[J]. Laser & Infrared, 2021, 51(8): 1081-1087. DOI: 10.3969/j.issn.1001-5078.2021.08.018
    [2]
    曹海杰, 刘宁, 许吉, 等. 红外图像自适应逆直方图增强技术[J]. 红外与激光工程, 2020, 49(4): 0426003. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ202004036.htm

    CAO Haijie, LIU Ning, XU Ji, et al. Infrared image adaptive inverse histogram enhancement technology[J]. Infrared and Laser Engineering, 2020, 49(4): 0426003. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ202004036.htm
    [3]
    HE K M, SUN J, TANG X O. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397-1409. DOI: 10.1109/TPAMI.2012.213
    [4]
    袁小燕, 张照锋, 顾振飞, 等. 基于大气散射模型的红外图像增强方法[J]. 电子器件, 2019, 42(1): 147-156. https://www.cnki.com.cn/Article/CJFDTOTAL-DZQJ201901029.htm

    YUAN Xiaoyan, ZHANG Zhaofeng, GU Zhenfei, et al. An infrared image enhancement method based on the atmospheric scattering model[J]. Chinese Journal of Electron Devices, 2019, 42(1): 147-156. https://www.cnki.com.cn/Article/CJFDTOTAL-DZQJ201901029.htm
    [5]
    ZHENG L, SHI H, GU M. Infrared traffic image enhancement algorithm based on dark channel prior and gamma correction[J]. Modern Physics Letters B, 2017, 31(19-21): 84-92.
    [6]
    朱珍, 黄锐, 臧铁钢, 等. 基于加权近红外图像融合的单幅图像除雾方法[J]. 计算机科学, 2020, 47(8): 241-244. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA202008038.htm

    ZHU Zhen, HUANG Rui, ZANG Tiegang, et al. Single image defogging method based on weighted near-infrared image fusion[J]. Computer Science, 2020, 47(8): 241-244. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA202008038.htm
    [7]
    沈瑜, 党建武, 苟吉祥, 等. 近红外与可见光双通道传感器信息融合的去雾技术[J]. 光谱学与光谱分析, 2019, 39(5): 1420-1427. https://www.cnki.com.cn/Article/CJFDTOTAL-GUAN201905018.htm

    SHEN Yu, DANG Jianwu, GOU Jixiang, et al. A dehaze algorithm based on near-infrared and visible dual channel sensor information fusion[J]. Spectroscopy and Spectral Analysis, 2019, 39(5): 1420-1427. https://www.cnki.com.cn/Article/CJFDTOTAL-GUAN201905018.htm
    [8]
    韩松臣, 黄畅昕, 李炜, 等. 一种改进的基于近红外图像的去雾方法[J]. 工程科学与技术, 2018, 50(2): 99-104. https://www.cnki.com.cn/Article/CJFDTOTAL-SCLH201802012.htm

    HAN Songchen, HUANG Changxin, LI Wei, et al An improved dehazing algorithm based on near infrared image[J]. Advanced Engineering Sciences, 2018, 50(2): 99-104. https://www.cnki.com.cn/Article/CJFDTOTAL-SCLH201802012.htm
    [9]
    HE K M, SUN J, TANG X O. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33 (12): 2341-2353. DOI: 10.1109/TPAMI.2010.168
    [10]
    LI Y, ZHANG Y F, GENG A H, et al. Infrared image enhancement based on atmospheric scattering model and histogram equalization[J]. Optics & Laser Technology, 2016, 83(9): 99-107.
    [11]
    梁恩辉, 周安然, 裴继红, 等. 基于能量图的海上红外图像目标分割方法[J]. 指挥信息系统与技术, 2018, 9(2): 79-84. https://www.cnki.com.cn/Article/CJFDTOTAL-ZHXT201802015.htm

    LIANG Enhui, ZHOU Enron, PEI Jihong, et al. Maritime infrared image target segmentation method based on energy map[J]. Command Information System And Technology, 2018, 9(2): 79-84. https://www.cnki.com.cn/Article/CJFDTOTAL-ZHXT201802015.htm
    [12]
    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. http://hwjs.nvir.cn/article/id/hwjs202006007

    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. http://hwjs.nvir.cn/article/id/hwjs202006007
    [13]
    余佩伦, 施佺, 王晗. 并行生成网络的红外—可见光图像转换[J]. 中国图象图形学报, 2021, 26(10): 2346-2356. DOI: 10.11834/jig.200113

    YU P L, SHI Q, WANG H. Infrared-to-visible image translation based on parallel generator network[J]. Journal of Image and Graphics, 2021, 26(10): 2346-2356. DOI: 10.11834/jig.200113
    [14]
    全雪峰. 基于自适应大气光校正的图像去雾方法[J]. 计算机应用与软件, 2019, 36(3): 104-111. https://www.cnki.com.cn/Article/CJFDTOTAL-JYRJ201903021.htm

    QUAN Xuefeng. Image dehazing based on adaptive atmospheric light correction[J]. Computer Applications and Software, 2019, 36(3): 04-111. https://www.cnki.com.cn/Article/CJFDTOTAL-JYRJ201903021.htm
    [15]
    HAUTIERE N, TAREL J P, AUBERT D, et al. Blind contrast enhancement assessment by gradient ratioing at visible edges[J]. Image Analysis & Stereology Journal, 2008, 27(2): 87-95.
    [16]
    WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Trans Image Process, 2004, 13(4): 600-612.
  • Related Articles

    [1]DAI Yueming, YANG Lufeng, TONG Xiongmin. Real-time Section State Verification Method of Energy Management System Low Voltage Equipment Based on Infrared Image and Deep Learning[J]. Infrared Technology , 2024, 46(12): 1464-1470.
    [2]CHEN Qiuyan, ZHANG Xinyan, HE Min, TIAN Yichun, LIU Ning, GUO Rui, WANG Xiaohui, YOU Siyuan, ZHANG Xiukun. Identification of Pipeline Thermal Image Leakage Based on Deep Learning[J]. Infrared Technology , 2024, 46(5): 522-531.
    [3]DUAN Jin, ZHANG Hao, SONG Jingyuan, LIU Ju. Review of Polarization Image Fusion Based on Deep Learning[J]. Infrared Technology , 2024, 46(2): 119-128.
    [4]FU Tian, DENG Changzheng, HAN Xinyue, GONG Mengqing. Infrared and Visible Image Registration for Power Equipments Based on Deep Learning[J]. Infrared Technology , 2022, 44(9): 936-943.
    [5]ZHANG Yutong, ZHAI Xuping, NIE Hong. Deep Learning Method for Action Recognition Based on Low Resolution Infrared Sensors[J]. Infrared Technology , 2022, 44(3): 286-293.
    [6]ZHONG Rui, YANG Li, DU Yongcheng. The Influence of Deep Transfer Learning Pre-training on Infrared Wake Image Recognition[J]. Infrared Technology , 2021, 43(10): 979-986.
    [7]HE Qian, LIU Boyun. Review of Infrared Image Edge Detection Algorithms[J]. Infrared Technology , 2021, 43(3): 199-207.
    [8]FAN Peng, FENG Wanxing, ZHOU Ziqiang, ZHAO Chun, ZHOU Sheng, YAO Xiangyu. Application of Deep Learning in Abnormal Insulator Infrared Image Diagnosis[J]. Infrared Technology , 2021, 43(1): 51-55.
    [9]YANG Tao, DAI Jun, WU Zhongjian, JIN Daizhong, ZHOU Guojia. Target Recognition of Infrared Ship Based on Deep Learning[J]. Infrared Technology , 2020, 42(5): 426-433.
    [10]JIAO Anbo, HE Miao, LUO Haibo. Research on Significant Edge Detection of Infrared Image Based on Deep Learning[J]. Infrared Technology , 2019, 41(1): 72-77.
  • Cited by

    Periodical cited type(4)

    1. 陈秋菊,彭天昊,康万杰,何国锋. 特征融合的电力机械设备过热故障红外检测. 机械设计与制造. 2024(04): 337-341 .
    2. 尚华胜,甘淑,袁希平,朱智富,李绕波. 级联语义分割和边缘检测的GF-2影像耕地提取. 遥感信息. 2024(04): 134-143 .
    3. 刘志东. 改进神经网络的船舶红外图像边缘检测方法. 舰船科学技术. 2023(07): 166-169 .
    4. 浦莹开,张笃振. 一种细化边缘的轻量级边缘检测神经网络. 计算机应用研究. 2023(11): 3485-3489 .

    Other cited types(4)

Catalog

    Article views PDF downloads Cited by(8)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return