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非制冷红外图像降噪算法综述

王加 周永康 李泽民 王世锦 曾邦泽 赵德利 胡健钏

王加, 周永康, 李泽民, 王世锦, 曾邦泽, 赵德利, 胡健钏. 非制冷红外图像降噪算法综述[J]. 红外技术, 2021, 43(6): 557-565.
引用本文: 王加, 周永康, 李泽民, 王世锦, 曾邦泽, 赵德利, 胡健钏. 非制冷红外图像降噪算法综述[J]. 红外技术, 2021, 43(6): 557-565.
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.

非制冷红外图像降噪算法综述

详细信息
    作者简介:

    王加(1989-),男,汉族,硕士研究生。主要研究方向:红外图像处理及相关技术。E-mail:wj2abcde234@163.com

  • 中图分类号: TP751.1

A Survey of Uncooled Infrared Image Denoising Algorithms

  • 摘要: 红外图像处理中,由于非制冷红外探测器工艺技术上的原因,原始的红外图像包含多种噪声,尤其是椒盐噪声、固定或随机条纹噪声。当前有许多红外图像降噪的滤波算法,但在时间、空间、降噪效果、细节保持等方面各有侧重,难以实现完美结合。如何更快速、更高效、更准确地滤除噪声信息,保留更多的细节信息,是今后红外图像处理降噪研究的关键方向。本文调研了目前主流的红外图像降噪算法,并从传统滤波降噪、变换域滤波降噪、基于图像分层处理滤波降噪三大类别进行了分析比较,并且提出了一种结合传统算法和基于图像分层的自适应降噪算法,为今后的相关领域研究人员提供参考。
  • 图  1  红外图像分层处理基本框架

    Figure  1.  Basic framework of infrared image layered processing

    图  2  引导滤波细节层处理效果对比图

    Figure  2.  Comparison of processing effect of detail layer of guided filtering

    图  3  双边滤波细节层处理效果对比图

    Figure  3.  Comparison of processing effect of detail layer of bilateral filtering

    图  4  加权最小二乘滤波细节层处理效果对比图

    Figure  4.  Comparison of processing effect of detail layer of weighted least square filtering

    图  5  自适应降噪系数细节层处理效果对比图

    Figure  5.  Comparison of processing effect of detail layer of adaptive gain coefficient

    图  6  三种分层处理滤波器结合中值滤波器与自适应系数处理效果对比

    Figure  6.  Comparison of three kinds of layered filter and median filter

    表  1  三类基于分层处理算法优缺点对比

    Table  1.   Comparison of advantages and disadvantages of three kinds of layered processing algorithms

    Type Algorithm Advantages Disadvantages
    Conventional filtering Gaussian filtering Simple algorithm Insufficient noise reduction effect
    Mean value filtering Easy to implement, suitable for particle noise Will blur the image
    Median filtering Good effect on pepper noise treatment Lack of detail noise handling
    Transformation domain filtering Wavelet transform Good detail retention Algorithm transformation with many steps
    Contourlet transform Good effect of complex noise treatment Long computation time for algorithms
    BM3D algorithm Good noise suppression and detail retention Insufficient real-time algorithm
    Based on image layering filtering Guided filtering Good layering effect and fast running speed Detailing still needs to be improved
    Bilateral filtering Better background separation Longer running time of the algorithm
    Weighted least squares Better detail retention Longer processing time
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出版历程
  • 收稿日期:  2020-06-22
  • 修回日期:  2020-07-28
  • 刊出日期:  2021-06-20

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