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基于FPGA的低照度条件下EBAPS图像混合噪声去除算法

夏皓天 钱芸生 王逸伦 郎怡政

夏皓天, 钱芸生, 王逸伦, 郎怡政. 基于FPGA的低照度条件下EBAPS图像混合噪声去除算法[J]. 应用光学, 2022, 43(6): 1075-1087. doi: 10.5768/JAO202243.0604006
引用本文: 夏皓天, 钱芸生, 王逸伦, 郎怡政. 基于FPGA的低照度条件下EBAPS图像混合噪声去除算法[J]. 应用光学, 2022, 43(6): 1075-1087. doi: 10.5768/JAO202243.0604006
XIA Haotian, QIAN Yunsheng, WANG Yilun, LANG Yizheng. Mixed noise removal algorithm of EBAPS image under low illumination condition based on FPGA[J]. Journal of Applied Optics, 2022, 43(6): 1075-1087. doi: 10.5768/JAO202243.0604006
Citation: XIA Haotian, QIAN Yunsheng, WANG Yilun, LANG Yizheng. Mixed noise removal algorithm of EBAPS image under low illumination condition based on FPGA[J]. Journal of Applied Optics, 2022, 43(6): 1075-1087. doi: 10.5768/JAO202243.0604006

基于FPGA的低照度条件下EBAPS图像混合噪声去除算法

doi: 10.5768/JAO202243.0604006
基金项目: 国家自然科学基金“叶企孙”科学基金项目(U2141239)
详细信息
    作者简介:

    夏皓天(1998—),男,硕士研究生,主要从事光电信息探测、微光成像技术与微光图像处理研究。E-mail:xiahaotian0422@163.com

    通讯作者:

    钱芸生(1968—),男,教授,博导,主要从事光电成像器件、系统与相关测试技术研究。 E-mail:yshqian@njust.edu.cn

  • 中图分类号: TN206

Mixed noise removal algorithm of EBAPS image under low illumination condition based on FPGA

  • 摘要: 为了解决单一的中值滤波和高斯滤波算法对低照度图像中同时存在的脉冲噪声和泊松噪声抑制效果不佳、边缘细节保护不足的问题,提出一种基于可编程逻辑门阵列(field programmable gate array,FPGA)的开关中值-高斯融合(open and close mix-median-Gaussian,OCMMG)滤波算法。首先,利用最小四方向差值检测每个像素点的异常程度,根据脉冲噪声判别阈值分配权重,进行第1步滤波处理;然后,利用四方向边缘检测算法提取图像边缘,根据设置的边缘置信度表征值进行第2步滤波处理;最后,用电子轰击有源像素传感器(electron bombarded active pixel sensor,EBAPS)在1×10−3 lx照度条件下采集的图像,基于FPGA对其进行实时图像处理。实验结果表明,FPGA处理结果与软件仿真处理结果相符。该算法相比于中值滤波和高斯滤波算法,峰值信噪比分别提高了3.23%和16.34%,结构相似性分别提高了14.66%和33.86%,边缘保持指数分别提高了0.49%和4.21%,能够有效去除EBAPS图像的混合噪声,并满足实时性要求。
  • 图  1  系统总体框架设计

    Fig.  1  Design diagram of overall framework for system

    图  2  算法模块流程图

    Fig.  2  Flow chart of algorithm module

    图  3  3×3模板生成图

    Fig.  3  Diagram of 3×3 template generation

    图  4  3×3矩阵映射图

    Fig.  4  Diagram of 3×3 matrix mapping

    图  5  最小四方向差值流程图

    Fig.  5  Flow chart of minimum four-direction differences

    图  6  快速中值滤波模块流程图

    Fig.  6  Flow chart of fast median filter module

    图  7  边缘检测模块流程图

    Fig.  7  Flow chart of edge detection module

    图  8  高斯滤波模板

    Fig.  8  Diagram of gaussian filter template

    图  9  仿真测试图

    Fig.  9  Diagram of simulation test

    图  10  照度1的去噪结果对比图

    Fig.  10  Comparison diagram of denoising results of illuminance 1

    图  11  照度2的去噪结果对比图

    Fig.  11  Comparison diagram of denoising results of illuminance 2

    图  12  照度3的去噪结果对比图

    Fig.  12  Comparison diagram of denoising results of illuminance 3

    图  13  硬件设备及其内部结构图

    Fig.  13  Hardware device and its internal structure diagram

    图  14  高压电源与照度计

    Fig.  14  High-voltage power supply and illuminometer

    图  15  静态靶标实验结果对比图

    Fig.  15  Comparison of experimental results of static targets

    图  16  静态SF实验结果对比图

    Fig.  16  Comparison of experimental results of static SF graphics

    图  17  夜晚外部环境实验结果对比图

    Fig.  17  Comparison of experimental results of external environment at night

    图  18  运动目标实验结果对比图

    Fig.  18  Comparison of experimental results of moving targets

    图  19  仿真软件图像处理运行时间

    Fig.  19  Image processing running time of simulation software

    图  20  FPGA图像处理运行时间

    Fig.  20  Image processing running time of FPGA

    表  1  照度1的测试图去噪后评价结果

    Table  1  Evaluation results of test map after denoising of illuminance 1

    滤波算法LenaGoldhillPepper
    PSNRSSIMEPIPSNRSSIMEPIPSNRSSIMEPI
    噪声图21.499 40.294 20.906 421.385 50.412 40.911 921.361 20.322 50.924 4
    中值滤波31.966 50.514 30.990 629.479 40.588 50.984 831.131 60.490 20.991 3
    高斯滤波28.930 40.460 00.980 927.960 80.570 80.978 328.330 80.452 90.983 4
    文献[24]29.537 00.450 80.983 828.520 80.588 30.981 328.875 00.467 00.985 6
    文献[25]28.824 40.519 30.986 927.398 70.616 40.983 127.562 00.494 50.987 0
    本文算法33.006 80.583 20.993 631.176 60.685 40.989 732.237 30.560 40.993 3
    下载: 导出CSV

    表  2  照度2的测试图去噪后评价结果

    Table  2  Evaluation results of test map after denoising of illuminance 2

    滤波算法LenaGoldhillPepper
    PSNRSSIMEPIPSNRSSIMEPIPSNRSSIMEPI
    噪声图21.131 30.164 50.796 321.633 60.225 30.752 021.848 40.177 30.787 9
    中值滤波28.558 60.401 00.984 033.145 60.479 30.974 033.880 70.378 80.981 7
    高斯滤波27.195 80.322 80.955 729.416 40.407 50.940 129.759 30.317 60.953 2
    文献[24]27.253 90.304 60.966 930.225 40.409 20.951 430.282 30.307 10.959 8
    文献[25]28.669 00.409 10.979 431.267 90.501 60.969 130.906 60.377 30.973 3
    本文算法28.737 50.442 10.987 234.417 50.567 10.980 634.791 60.431 00.985 2
    下载: 导出CSV

    表  3  照度3的测试图去噪后评价结果

    Table  3  Evaluation results of test map after denoising of illuminance 3

    滤波算法LenaGoldhillPepper
    PSNRSSIMEPIPSNRSSIMEPIPSNRSSIMEPI
    噪声图21.644 90.126 40.608 421.617 50.194 00.674 521.549 40.138 90.653 9
    中值滤波35.779 90.345 50.968 834.227 30.451 80.968 535.414 40.342 80.974 0
    高斯滤波29.757 10.260 30.888 729.529 60.365 90.913 529.631 20.261 90.908 9
    文献[24]31.664 70.257 50.925 731.170 10.374 80.940 031.350 60.267 50.937 8
    文献[25]34.312 50.354 40.962 232.972 50.482 40.964 633.657 70.350 50.967 3
    本文算法36.776 00.387 90.975 235.399 40.531 80.975 936.527 80.388 70.979 9
    下载: 导出CSV

    表  4  仿真软件与FPGA算法客观评价一致性检验

    Table  4  Consistency test of objective evaluation of simulation software and FPGA algorithm

    参数PSNRSSIMEPI
    仿真软件FPGA误差/%仿真软件FPGA误差/%仿真软件FPGA误差/%
    靶标1×10−1 lx32.606 732.384 30.680.604 60.647 76.650.958 30.993 43.53
    靶标1×10−2 lx30.476 230.384 30.300.529 70.577 08.200.931 50.955 02.46
    靶标1×10−3 lx34.291 634.168 50.360.527 50.564 66.570.930 20.938 10.84
    SF 1×10−1 lx33.197 833.047 70.450.563 60.605 36.890.982 90.995 51.27
    SF 1×10−2 lx30.779 730.604 90.570.535 40.554 93.510.907 80.968 66.28
    SF 1×10−3 lx35.385 035.668 30.790.505 60.561 19.890.933 70.960 12.75
    夜晚外部环境35.004 335.368 41.030.617 80.635 32.750.989 20.997 00.78
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-08-01
  • 修回日期:  2022-09-24
  • 网络出版日期:  2022-10-11
  • 刊出日期:  2022-11-14

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