留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于子窗口盒式滤波的MPPC微光成像增强算法

汤祥裕 尹丽菊 周辉 邹国锋 崔玉敏 邓玉林 张言华

汤祥裕, 尹丽菊, 周辉, 邹国锋, 崔玉敏, 邓玉林, 张言华. 基于子窗口盒式滤波的MPPC微光成像增强算法[J]. 应用光学, 2022, 43(6): 1097-1106. doi: 10.5768/JAO202243.0604008
引用本文: 汤祥裕, 尹丽菊, 周辉, 邹国锋, 崔玉敏, 邓玉林, 张言华. 基于子窗口盒式滤波的MPPC微光成像增强算法[J]. 应用光学, 2022, 43(6): 1097-1106. doi: 10.5768/JAO202243.0604008
TANG Xiangyu, YIN Liju, ZHOU Hui, ZOU Guofeng, CUI Yumin, DENG Yulin, ZHANG Yanhua. MPPC low-level-light imaging enhancement algorithm based on sub-window box filtering[J]. Journal of Applied Optics, 2022, 43(6): 1097-1106. doi: 10.5768/JAO202243.0604008
Citation: TANG Xiangyu, YIN Liju, ZHOU Hui, ZOU Guofeng, CUI Yumin, DENG Yulin, ZHANG Yanhua. MPPC low-level-light imaging enhancement algorithm based on sub-window box filtering[J]. Journal of Applied Optics, 2022, 43(6): 1097-1106. doi: 10.5768/JAO202243.0604008

基于子窗口盒式滤波的MPPC微光成像增强算法

doi: 10.5768/JAO202243.0604008
基金项目: 国家自然科学基金(62101310);山东自然科学基金(ZR2020MF127)
详细信息
    作者简介:

    汤祥裕(1996—),男,硕士研究生,主要从事微光图像处理方面的研究。E-mail:1275821162@qq.com

    通讯作者:

    尹丽菊(1972—),女,教授,博士,主要从事光电检测技术、成像探测与机器视觉方面的研究。E-mail:Ljyin72@163.com

  • 中图分类号: TN223

MPPC low-level-light imaging enhancement algorithm based on sub-window box filtering

  • 摘要: 针对多像素光子计数器(MPPC)进行微光成像时,图像受光照不足和噪声影响出现的图像亮度低、对比度差、边缘模糊等问题,提出一种基于子窗口盒式滤波的自适应微光图像处理算法。为了减少算法运行时间的同时突出图像的边缘细节信息,利用子窗口盒式滤波器对图像进行分层得到基础层和细节层;对基础层图像采用自适应阈值直方图均衡化拉伸对比度,细节层图像采用自适应增益控制方式进行增强;根据基础层图像中有效灰度值个数占总灰度的比值自适应确定融合系数,将基础层图像与细节层图像融合得到增强后图像。通过微光实验平台设置3组不同照度的微光环境进行实验仿真,验证了本文算法在保持边缘信息和增强细节方面获得了更好的效果。实验结果表明本文算法在标准差、信息熵、平均梯度等客观评价方面优于改进前算法,提升了微光图像的成像效果。
  • 图  1  不同类型支持区域的滤波特征

    Fig.  1  Diagram of filtering characteristics in different types of support regions

    图  2  中心像素位于8个子窗口的位置

    Fig.  2  Center pixel located in position of eight sub windows

    图  3  不同参数条件下的子窗口盒式滤波器

    Fig.  3  Sub-window box filter under different parameter conditions

    图  4  子窗口盒式滤波器运算流程

    Fig.  4  Flow chart of sub-window box filter operation

    图  5  引导滤波和本文滤波器保持边缘性能对比

    Fig.  5  Performance comparison in edge preserving between guided filter and proposed filter

    图  6  本文微光图像增强算法流程

    Fig.  6  Flow chart of proposed low-level-light image enhancement algorithm

    图  7  算法处理前后的微光图像直方图分布

    Fig.  7  Histogram distribution of low-level-light image before and after algorithm processing

    图  8  累计直方图概率分布

    Fig.  8  Cumulative histogram probability distribution

    图  9  直方图均衡化效果对比

    Fig.  9  Effect comparison of histogram equalization in different scenes

    图  10  微光图像细节层增强

    Fig.  10  Detail layer enhancement of low-level-light image

    图  11  实验平台实物图

    Fig.  11  Physical drawing of experimental platform

    图  12  实验平台内部结构

    Fig.  12  Internal structure of experimental platform

    图  13  不同照度下获取的原始图像

    Fig.  13  Raw images obtained at different levels of illumination

    图  14  ${10^{ - 1}}\;{\rm{lx}}$微光环境下各算法增强图像效果对比

    Fig.  14  Comparison of effect of image enhancement by algorithms under ${10^{ - 1}}\;{\rm{lx}}$ low-level-light condition

    图  15  ${10^{ - 3}}\;{\rm{lx}}$微光环境下各算法增强图像效果对比

    Fig.  15  Comparison of effect of image enhancement by algorithms under ${10^{ - 3}}\;{\rm{lx}}$ low-level-light condition

    图  16  ${10^{ - 5}}\;{\rm{lx}}$微光环境下各算法增强图像效果对比

    Fig.  16  Comparison of effect of image enhancement by algorithms under ${10^{ - 5}}\;{\rm{lx}}$ low-level-light condition

    表  1  ${10^{ - 1}}\;{\rm{lx}}$微光环境下的图像增强评价指标

    Table  1  Image enhancement evaluation indexes under ${10^{ - 1}}\;{\rm{lx}}$ low-level-light environment

    算法标准差信息熵平均梯度
    ABCABCABC
    原始图像34.6315.8729.246.381.204.591.260.570.83
    交叉子窗
    口算法
    35.5114.4333.756.411.224.981.370.610.96
    DDE算法35.8216.1133.566.461.164.811.780.721.06
    文献[4]算法37.3517.1536.576.471.194.941.430.891.12
    本文算法39.5820.5640.366.490.775.192.271.151.46
    下载: 导出CSV

    表  2  ${10^{ - 3}}\;{\rm{lx}}$微光环境下的图像增强评价指标

    Table  2  Image enhancement evaluation indexes under ${10^{ - 3}}\;{\rm{lx}}$ low-level-light environment

    算法标准差信息熵平均梯度
    ABCABCABC
    原始图像21.2916.7119.425.851.816.040.770.690.64
    交叉子窗
    口算法
    22.3917.2419.576.041.736.060.870.720.68
    DDE算法24.6718.0819.655.871.665.981.060.800.71
    文献[4]算法26.8918.7019.826.011.636.010.980.870.78
    本文算法28.9419.3028.436.151.396.391.180.951.13
    下载: 导出CSV

    表  3  ${10^{ - 5}}\;{\rm{lx}}$微光环境下的图像增强评价指标

    Table  3  Image enhancement evaluation indexes under ${10^{ - 5}}\;{\rm{lx}}$ low-level-light environment

    算法标准差信息熵平均梯度
    ABCABCABC
    原始图像15.539.178.085.271.554.550.450.370.26
    交叉子窗
    口算法
    14.8810.1014.075.281.384.610.420.430.41
    DDE算法17.1510.5711.595.241.304.580.580.440.37
    文献[4]算法17.4611.2312.025.461.274.610.600.480.42
    本文算法16.5712.4815.165.211.323.890.630.570.51
    下载: 导出CSV

    表  4  4种算法运行时间对比

    Table  4  Comparison of running time of four algorithms s

    算法微光环境
    $ {10^{ - 1}}\;{\rm{lx}} $$ {10^{ - 3}}\;{\rm{lx}} $$ {10^{ - 5}}\;{\rm{lx}} $
    交叉子窗口算法10.3719.1146.947
    文献[4]算法9.2107.0623.763
    本文算法2.8162.1771.041
    下载: 导出CSV
  • [1] 徐炜君, 刘国忠. 空间域和频域结合的图像增强技术及实现[J]. 中国测试,2009,35(4):52-54.

    XU Weijun, LIU Guozhong. Image enhancement technology combining spatial domain and frequency domain and its implementation[J]. China Measurement & Test,2009,35(4):52-54.
    [2] 周克虎, 雷涛, 罗刚. 一种基于时域滤波的红外序列图像去噪算法[J]. 应用光学,2021,42(3):474-480.

    ZHOU Kehu, LEI Tao, LUO Gang. An infrared sequence image denoising algorithm based on time-domain filtering[J]. Journal of Applied Optics,2021,42(3):474-480.
    [3] LIU Ning, ZHAO Dongxue. Detail enhancement for high-dynamic-range infrared images based on guided image filter[J]. Infrared Physics & Technology,2014,67:138-147.
    [4] ZHOU B, LUO Y, YANG M, et al. An improved adaptive detail enhancement algorithm for infrared images based on guided image filter[J]. Journal of Modern Optics,2019,66(1):33-46. doi: 10.1080/09500340.2018.1511861
    [5] 王炫, 尹丽菊, 高明亮, 等. 基于新符号函数与盲源分离的光子计数图像去噪方法[J]. 激光与光电子学进展. 2018, 55(10): 158-164.

    WANG Xuan, YIN Liju, GAO Mingliang, et al. Photon counting image denoising method based on new symbol function and blind source separation[J]. Laser & Optoelectronics Progress. 2018, 55(10): 158-164.
    [6] 廖斌, 刘鸳鸳. 基于多尺度灰度变换的图像增强研究[J]. 量子电子学报,2015,32(5):550-554.

    LIAO Bin, LIU Yuanyuan. Research on image enhancement based on multi-scale gray transform[J]. Chinese Journal of Quantum Electronics,2015,32(5):550-554.
    [7] YIN Hui, GONG Yuanhao, Qiu Guoping. Combined window filtering and its applications[J]. Multidimensional Systems and Signal Processing,2021,32(2):313-333.
    [8] KITAGAWA G. The two-filter formula for smoothing and an implementation of the Gaussian-sum smoother[J]. Annals of the Institute of Statistical Mathematics,1994,46:605-623. doi: 10.1007/BF00773470
    [9] 沈德海, 张龙昌, 鄂旭. 基于多子窗口的混合噪声滤波算法[J]. 计算机技术与发展,2015,25(6):69-72.

    SHEN Dehai, ZHANG Longchang, E Xu. Hybrid noise filtering algorithm based on multiple sub-windows[J]. Computer Technology and Development,2015,25(6):69-72.
    [10] CÁTIA P, ANA O, CRISTINA J, et al. Automatic crackle detection algorithm based on fractal dimension and box filtering[J]. Procedia Computer Science,2015,64:705-712. doi: 10.1016/j.procs.2015.08.592
    [11] 胡海波, 邱民仆. 微光图像对比度增强技术研究[J]. 测绘通报,2015,1:26-30.

    HU Haibo, QIU Minpu. Contrast enhancement of low light level image[J]. Bulletin of Surveying and Mapping,2015,1:26-30.
    [12] 孙国栋, 徐昀, 徐亮. 不均匀光照和重影的仪表图像二值化方法[J]. 应用光学,2020,41(1):74-78. doi: 10.5768/JAO202041.0102002

    SUN Guodong, XU Yun, XU Liang. Instrumentation image binarization method for inhomogeneous illumination and ghosting[J]. Journal of Applied Optics,2020,41(1):74-78. doi: 10.5768/JAO202041.0102002
    [13] 汪伟, 许德海, 任明艺. 一种改进的红外图像自适应增强方法[J]. 红外与激光工程,2021,50(11):419-427.

    WANG Wei, XU Dehai, REN Mingyi. An improved adaptive enhancement method for infrared image[J]. Infrared and Laser Engineering,2021,50(11):419-427.
    [14] 李佳, 李少娟, 段小虎, 等. 基于Retinex理论与概率非局部均值的红外图像增强方法[J]. 光子学报,2020,49(4):187-196.

    LI jia, LI Shaojuan, DUAN Xiaohu, et al. Infrared image enhancement method based on Retinex theory and probabilistic non-local mean[J]. Acta Photonica Sinica,2020,49(4):187-196.
    [15] 汪子君, 罗渊贻, 蒋尚志, 等. 基于引导滤波的自适应红外图像增强改进算法[J]. 光谱学与光谱分析,2020,40(11):3463-3467.

    WANG Zijun, LUO Yuanyi, JIANG Shangzhi, el at. An improved adaptive infrared image enhancement algorithm based on guided filtering[J]. Spectroscopy and Spectral Analysis,2020,40(11):3463-3467.
  • 加载中
图(16) / 表(4)
计量
  • 文章访问数:  105
  • HTML全文浏览量:  60
  • PDF下载量:  9
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-05-05
  • 修回日期:  2022-06-20
  • 网络出版日期:  2022-09-24
  • 刊出日期:  2022-11-14

目录

    /

    返回文章
    返回