Advanced Search
中文
Volume 43 Issue 6
Jun.  2021
Turn off MathJax
Article Contents
Abstract
References
WANG Jia, ZHOU Yongkang, LI Zemin, WANG Shijin, ZENG Bangze, ZHAO Deli, HU Jianchuan. A Survey of Uncooled Infrared Image Denoising Algorithms[J]. Infrared Technology , 2021, 43(6): 557-565.
Citation: WANG Jia, ZHOU Yongkang, LI Zemin, WANG Shijin, ZENG Bangze, ZHAO Deli, HU Jianchuan. A Survey of Uncooled Infrared Image Denoising Algorithms[J]. Infrared Technology , 2021, 43(6): 557-565.
shu

A Survey of Uncooled Infrared Image Denoising Algorithms

  • 1.

    Kunming Institute of Physics, Kunming 650223, China

  • 2.

    The First Military Representative Office in Kunming of the Army Department in Chongqing, Kunming 650032, China

More Information
  • Received Date: June 21, 2020
  • Revised Date: July 27, 2020
    Graphical Abstract
    • Abstract
  • In infrared image processing, owing to technical issues with the infrared detector, the original infrared image includes a variety of noise, especially salt and pepper noise, fixed noise, or random stripe noise. Currently, there are many filtering algorithms for infrared image denoising, but they emphasize time, space, denoising effect, maintaining detail, and so on differently; therefore, it is difficult to achieve a perfect combination. Identifying methods to filter noise information more quickly, efficiently, and accurately and retain more details is an important future research direction for noise reduction in infrared image processing. This study investigated and compared the current mainstream infrared image denoising algorithms from three categories: traditional filter denoising, transform domain filter denoising, and image layered processing filter denoising, and a combination of a traditional algorithm and image layered adaptive denoising algorithm is proposed to provide a reference for future studies in related fields.
    • FullText(HTML)
    • References (21)
  • [1]
    王海菊, 谭常玉, 王坤林, 等. 自适应高斯滤波图像去噪算法[J]. 福建电脑, 2017, 33(11): 5-6. https://www.cnki.com.cn/Article/CJFDTOTAL-FJDN201711002.htm

    WANG Haiju, TAN Changyu, WANG Kunlin, et al. Image denoising algorithm based on adaptive Gauss filter[J]. Fujian Computer, 2017, 33(11): 5-6. https://www.cnki.com.cn/Article/CJFDTOTAL-FJDN201711002.htm
    [2]
    LUO X, WANG S, LI B, et al. A mixed noise filter acceleration algorithm based on mean value and variance similarity[C]//2011 Second International Conference on Digital Manufacturing & Automation, 2011: 55-58(doi: 10.1109/ICDMA.2011.22).
    [3]
    HSIEH M H, CHENG F C, SHIE M C, et al. Fast and efficient median filter for removing 1%~99% levels of salt-and-pepper noise in images[J]. Engineering Applications of Artificial Intelligence, 2013, 26(4): 1333-1338. DOI: 10.1016/j.engappai.2012.10.012
    [4]
    刘杰, 张建勋, 代煜. 基于多引导滤波的图像增强算法[J]. 物理学报, 2018, 67(23): 293-302. https://www.cnki.com.cn/Article/CJFDTOTAL-WLXB201823030.htm

    LIU Jie, ZHANG Jianxun, DAI Yu. Image enhancement based on multi -guided filtering[J]. Acta Physica Sinica, 2018, 67(23): 293-302. https://www.cnki.com.cn/Article/CJFDTOTAL-WLXB201823030.htm
    [5]
    ZUO C, CHEN Q, LIU N. Display and detail enhancement for high-dynamic-range infrared images[J]. Opt. Eng., 2011, 50(12): 127401. DOI: 10.1117/1.3659698
    [6]
    Farbman Z, Fattal R, Lischinski D, et al. Edge-preserving decompositions for multi-scale tone and detail manipulation[J/OL]. ACM Transactions on Graphics, 2008, 27(3): https: //doi.org/10.1145/1360612.1360666.
    [7]
    Ismael S H, Mustafa F M, Okümüs I T. A new approach of image denoising based on discrete wavelet transform[C]//Computer Applications & Research of IEEE, 2016: doi: 10.1109/WSCAR.2016.30
    [8]
    DoM N, Vetterli M. Contourlets: directional multi⁃resolution Image representation[C]//International Conference on Image Processing, 2002(1): 357-360.
    [9]
    Dabov K, Foi A, Katkovnik V, et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing, 2007, 16(8): 2080-2095. DOI: 10.1109/TIP.2007.901238
    [10]
    李健, 丁小奇, 陈光, 等. 基于改进高斯滤波算法的叶片图像去噪方法[J]. 南方农业学报, 2019, 50(6): 1385-1391. DOI: 10.3969/j.issn.2095-1191.2019.06.31

    LI Jian, DING Xiaoqi, CHEN Guang. et al. Blade image denoising method based on improved Gauss filtering algorithm[J]. Journal of Southern Agriculture, 2019, 50(6): 1385-1391. DOI: 10.3969/j.issn.2095-1191.2019.06.31
    [11]
    魏松涛, 何文思. 一种基于均值滤波和梯度影响因子的滤波算法[J]. 机械制造与自动化, 2020, 49(1): 20-22. https://www.cnki.com.cn/Article/CJFDTOTAL-ZZHD202001006.htm

    WEI Songtao, HE Wensi. Filtering algorithm based on mean filter and gradient influencing factor[J]. Machine Building & Automation, 2020, 49(1): 20-22. https://www.cnki.com.cn/Article/CJFDTOTAL-ZZHD202001006.htm
    [12]
    刘智嘉, 夏寅辉, 杨德振, 等. 基于中值滤波器的红外图像噪声处理的改进方法[J]. 激光与红外, 2019, 49(3): 376-380. https://www.cnki.com.cn/Article/CJFDTOTAL-JGHW201903019.htm

    LIU Zhijia, XIA Yinhui, YANG Dezhen, et al. An improved method for infrared image noise processing based on median filter[J]. Laser & Infrared, 2019, 49(3): 376-380. https://www.cnki.com.cn/Article/CJFDTOTAL-JGHW201903019.htm
    [13]
    葛朋, 杨波, 洪闻青, 等. 一种结合PE的高动态范围红外图像压缩及细节增强算法[J]. 红外技术, 2020, 42(3): 279-285. http://hwjs.nvir.cn/article/id/hwjs202003011

    GE Peng, YANG Bo, HONG Wenqing, et al. Dynamic range compression and detail enhancement algorithm combined with PE for high dynamic range infrared images[J]. Infrared Technology, 2020, 42(3): 279-285. http://hwjs.nvir.cn/article/id/hwjs202003011
    [14]
    朱才高. 红外图像增强算法研究及其DSP实现[D]. 南京: 南京理工大学, 2014.

    ZHU Caigao. Research on infrared image enhancement algorithm and Its DSP Implementation[D]. Nanjing: Nanjing University of Science And Technology, 2014.
    [15]
    LIU Ning, ZHAO Dongxue. Detail enhancement for high dynamic range infrared images based on guided image filter[J]. Infrared Physics and Technology, 2014, 67(7): 138-147
    [16]
    谢岱伟. 一种基于双边滤波的高动态红外图像压缩与细节增强算法[J]. 舰船电子对抗, 2019, 42(2): 96-98, 117. https://www.cnki.com.cn/Article/CJFDTOTAL-JCDZ201902021.htm

    XIE Daiwei. Analgorithm of high dynamic infrared image compression and detail strengthen based in bilateral filtering[J]. Shipboard Electronic Counter- Measure, 2019, 42(2): 96-98, 117. https://www.cnki.com.cn/Article/CJFDTOTAL-JCDZ201902021.htm
    [17]
    朱道广, 隋修宝, 朱才高, 等. 基于多尺度的高动态红外图像增强算法[J]. 红外技术, 2013, 35(8): 476-481, 486. http://hwjs.nvir.cn/article/id/hwjs201308005

    ZHU Daoguang, SUI Xiubao, ZHU Caigao, et al. Enhancement algorithm for high dynamic range infrared image based on multi-scale processing[J]. Infrared Technology, 2013, 35(8): 476-481, 486. http://hwjs.nvir.cn/article/id/hwjs201308005
    [18]
    周永康, 朱尤攀, 曾邦泽, 等. 宽动态红外图像增强算法综述[J]. 激光技术, 2018, 42(5): 718-726. https://www.cnki.com.cn/Article/CJFDTOTAL-JGJS201805025.htm

    ZHOU Yongkang, ZHU Youpan, ZENG Bangze, et al. A review for high dynamic range infrared image enhancement algorithms[J]. Laser Technology, 2018, 42(5): 718-726. https://www.cnki.com.cn/Article/CJFDTOTAL-JGJS201805025.htm
    [19]
    秦乾坤, 杨慧, 岳威, 等. 基于改进小波阈值的红外热波无损检测图像噪声抑制方法的研究[J]. 计算机时代, 2019(1): 75-78, 82. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJS201901022.htm

    QIN Qiankun, YANG Hui, YUE Wei, et al. Research on improved wavelet threshold based image noise suppression method for infrared thermal wave nondestructive testing[J]. Computer Era, 2019(1): 75-78, 82. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJS201901022.htm
    [20]
    王宝坤. 基于非下采样Contourlet变换的图像降噪研究[D]. 哈尔滨: 东北林业大学, 2018.

    WANG Baokun. Image Denoising Research Based on Nonsubsampled Contourlet Transform[D]. Harbin: Northeast Forestry University, 2018.
    [21]
    黄丽姝, 马铭志. 改进的基于BM3D的图像去噪算法[J]. 现代计算机(专业版), 2019(6): 60-63, 71. https://www.cnki.com.cn/Article/CJFDTOTAL-XDJS201906015.htm

    HUANG Lishu, MA Mingzhi. Improved image denoising algorithm based on BM3D[J]. Modern Computer (Pro), 2019(6): 60-63, 71. https://www.cnki.com.cn/Article/CJFDTOTAL-XDJS201906015.htm
    • Related Articles

  • Cited by

    Periodical cited type(6)

    1. 赵梓惠，周永康，曾邦泽，唐兴芬，傅志宇，殷永成. 红外图像降噪综述. 红外技术. 2025(03): 299-306 . 本站查看
    2. 宁大海，郑晟. 可见光和红外图像决策级融合目标检测算法. 红外技术. 2023(03): 282-291 . 本站查看
    3. 冯杰，张莹，叶影，贺润平，王哲斐. NSST域电气设备红外图像增强处理算法设计. 电子设计工程. 2023(21): 176-179+185 .
    4. 张桓，陈志盛. 多尺度自校正双直方图均衡化红外图像增强. 红外技术. 2023(12): 1207-1215 . 本站查看
    5. 金玫秀，朱士虎，王通，庄飞飞. 基于卤素灯激励的红外热成像裂纹无损检测研究. 红外技术. 2022(04): 421-427 . 本站查看
    6. 王东升，王海龙，张芳，韩林芳，赵怡琳. 基于时序信息的红外图像缺陷信息提取. 红外技术. 2022(06): 565-570 . 本站查看

    Other cited types(9)

Catalog

    Get Citation
    PDF
    XML
    Article views (1156) PDF downloads (404) Cited by(15)
    Related

    Copyright © 《Infrared Technology》Editorial Office滇ICP备12000354号-3

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return