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时空域自适应滤波非均匀性校正算法

郭玉婷 贾晓洪 李丽娟 刘俊明

郭玉婷, 贾晓洪, 李丽娟, 刘俊明. 时空域自适应滤波非均匀性校正算法[J]. 红外技术, 2023, 45(5): 482-487.
引用本文: 郭玉婷, 贾晓洪, 李丽娟, 刘俊明. 时空域自适应滤波非均匀性校正算法[J]. 红外技术, 2023, 45(5): 482-487.
GUO Yuting, JIA Xiaohong, LI Lijuan, LIU Junming. Space-Time Domain Adaptive Filtering Non-uniformity Correction Algorithm[J]. Infrared Technology , 2023, 45(5): 482-487.
Citation: GUO Yuting, JIA Xiaohong, LI Lijuan, LIU Junming. Space-Time Domain Adaptive Filtering Non-uniformity Correction Algorithm[J]. Infrared Technology , 2023, 45(5): 482-487.

时空域自适应滤波非均匀性校正算法

详细信息
    作者简介:

    郭玉婷(1998-)女,河南洛阳人,硕士研究生,主要研究方向为红外导引头信息处理技术。E-mail:641536183@qq.com

  • 中图分类号: TJ761.3

Space-Time Domain Adaptive Filtering Non-uniformity Correction Algorithm

  • 摘要: 由于红外焦平面探测器受到制造工艺等限制,图像不可避免地会存在非均匀性。传统神经网络算法会留下“鬼影”的问题,本文改进传统神经网络算法,利用引导滤波图像作为期望模板,防止图像的边缘被滤波器平滑。当场景运动时,通过时域迭代的策略来不断进行非均匀性校正参数的更新。为了抑制算法中常见的鬼影现象,设计了基于空域局部方差和时域场景变化率相结合的自适应学习率,利用前后的校正参数自适应调整阈值。实验仿真表明,本文所提的算法相比于传统算法均方根误差下降45.45%左右,可以在校正图像非均匀性的同时很好地抑制“鬼影”现象。
  • 图  1  神经网络法原理

    Figure  1.  Neural network method

    图  2  室内场景校正过程图

    Figure  2.  Indoor scene correction process diagram

    图  3  室内场景校正前后的图像粗糙度对比

    Figure  3.  Roughness comparison of indoor scene before and after correction

    图  4  室内场景校正后均方根误差对比

    Figure  4.  RMSE comparison of indoor scenes after correction

    图  5  室外场景校正过程图(每组图中左侧为传统神经网络算法,右侧是本文改进算法)

    Figure  5.  Outdoor scene correction process diagram(In each set of figures, the left is the traditional neural network algorithm, and the right is the improved algorithm in this paper)

    图  6  室外场景校正前后的图像粗糙度对比

    Figure  6.  Roughness comparison of outdoor scene before and after correction

    图  7  室外场景校正后均方根误差对比

    Figure  7.  RMSE comparison of outdoor scenes after correction

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出版历程
  • 收稿日期:  2022-04-19
  • 修回日期:  2022-06-02
  • 刊出日期:  2023-05-20

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