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一种基于粗糙集的红外图像多维降噪算法

王加 周永康 胡健钏 潘超 李泽民 曾邦泽 赵德利

王加, 周永康, 胡健钏, 潘超, 李泽民, 曾邦泽, 赵德利. 一种基于粗糙集的红外图像多维降噪算法[J]. 红外技术, 2021, 43(1): 44-50.
引用本文: 王加, 周永康, 胡健钏, 潘超, 李泽民, 曾邦泽, 赵德利. 一种基于粗糙集的红外图像多维降噪算法[J]. 红外技术, 2021, 43(1): 44-50.
WANG Jia, ZHOU Yongkang, HU Jianchuan, PAN Chao, LI Zemin, ZENG Bangze, ZHAO Deli. Infrared Image Denoising Algorithm Based on a Rough Set Approach[J]. INFRARED TECHNOLOGY, 2021, 43(1): 44-50.
Citation: WANG Jia, ZHOU Yongkang, HU Jianchuan, PAN Chao, LI Zemin, ZENG Bangze, ZHAO Deli. Infrared Image Denoising Algorithm Based on a Rough Set Approach[J]. INFRARED TECHNOLOGY, 2021, 43(1): 44-50.

一种基于粗糙集的红外图像多维降噪算法

详细信息
    作者简介:

    王加(1989-),男,硕士研究生,主要从事红外图像处理算法研究,E-mail: 80393796@qq.com

    通讯作者:

    赵德利(1989-),男,硕士,主要从事电路系统开发,E-mail: 719554525@qq.com

  • 中图分类号: TP751.1

Infrared Image Denoising Algorithm Based on a Rough Set Approach

  • 摘要: 针对红外图像噪声复杂多变,在抑制噪声的同时,还需要兼顾细节增强的问题,本文提出了一种基于粗糙集的红外图像多维降噪算法。对采集到的红外图像通过引导滤波进行分层后运用粗糙集理论进一步多维度的分层,分别处理后合并还原得到输出图像。综合对比主观观察与客观评价指标,该算法能够对红外图像降噪有良好效果,对弱小目标细节有良好的增强效果,另外,该算法复杂度较低,具有良好的实时性,在工程实现方面具有良好的应用前景。
  • 图  1  本文红外图像降噪算法框图

    Figure  1.  Infrared image denoising algorithm block diagram

    图  2  3×3滑动窗口

    Figure  2.  3×3 sliding window

    图  3  4个2×2滑动窗口之一

    Figure  3.  One of four 2×2 sliding windows

    图  4  引导滤波分层红外图像

    Figure  4.  Guided filtering layered infrared image

    图  5  粗糙集多维分层处理细节层红外图像

    Figure  5.  Multi dimensional hierarchical processing of detail layer infrared image based on rough set

    图  6  本文算法与对比红外图像

    Figure  6.  This algorithm is compared with infrared image

    图  7  不同算法效果条纹噪声抑制对比

    Figure  7.  Comparison of different algorithms for fringe noise suppression

    表  1  不同算法降噪效果客观评价指标

    Table  1.   Objective evaluation index of noise reduction effect of different algorithms

    Algorithm Peak Signal To Noise Ratio(PSNR) Entropy
    Guided filtering 54.5211 6.1239
    Bilateral filtering 55.3598 6.1274
    Least square filtering 57.7474 6.3115
    The algorithm in this paper 58.7371 6.4126
    下载: 导出CSV

    表  2  不同算法运行时间对比

    Table  2.   Running time comparison of different algorithms

    Algorithm Time complexity Running time/s
    Guided filtering O(N) 2.744
    Bilateral filtering O(σ2) 5.273
    Least square filtering O(mN) 4.397
    The algorithm in this paper O(N) 3.174
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-11-03
  • 修回日期:  2020-12-30
  • 刊出日期:  2021-01-20

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