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改进Canny算法的红外人脸图像边缘轮廓提取

王鑫刚 田军委 于亚琳 王沁 张杰

王鑫刚, 田军委, 于亚琳, 王沁, 张杰. 改进Canny算法的红外人脸图像边缘轮廓提取[J]. 应用光学, 2023, 44(1): 61-70. doi: 10.5768/JAO202344.0102001
引用本文: 王鑫刚, 田军委, 于亚琳, 王沁, 张杰. 改进Canny算法的红外人脸图像边缘轮廓提取[J]. 应用光学, 2023, 44(1): 61-70. doi: 10.5768/JAO202344.0102001
WANG Xin'gang, TIAN Junwei, YU Yalin, WANG Qin, ZHANG Jie. Edge contour extraction of infrared face image based on improved Canny algorithm[J]. Journal of Applied Optics, 2023, 44(1): 61-70. doi: 10.5768/JAO202344.0102001
Citation: WANG Xin'gang, TIAN Junwei, YU Yalin, WANG Qin, ZHANG Jie. Edge contour extraction of infrared face image based on improved Canny algorithm[J]. Journal of Applied Optics, 2023, 44(1): 61-70. doi: 10.5768/JAO202344.0102001

改进Canny算法的红外人脸图像边缘轮廓提取

doi: 10.5768/JAO202344.0102001
基金项目: 陕西省科技厅重点研发计划(2022GY-068,2022GY-316)
详细信息
    作者简介:

    王鑫刚(1998—),男,博士研究生,主要从事图像处理方面的研究。E-mail:3352577955@qq.com

    通讯作者:

    田军委(1973—),男,博士,教授,博导,主要从事数字图像处理,机器视觉与模式识别方面的研究。E-mail:tjwxjtu@126.com

  • 中图分类号: TN29;TP391

Edge contour extraction of infrared face image based on improved Canny algorithm

  • 摘要: 红外人脸图像的边缘轮廓特征对于红外人脸检测、识别等相关应用具有重要价值。针对红外人脸图像边缘轮廓提取时存在伪边缘的问题,提出了一种改进Canny算法的红外人脸图像边缘轮廓提取方法。首先通过对引导滤波算法引入“动态阈值约束因子”替换原始算法中的高斯滤波,解决了原始算法滤波处理不均匀和造成红外人脸图像弱边缘特征丢失的弊端;接着对原始算法的非极大值抑制进行了改进,在原始计算梯度方向的基础上又增加了4个梯度方向,使得非极大值抑制的插值较原始算法更加精细;最后改进OTSU(大津)算法,构造灰度-梯度映射函数确定最佳阈值,解决了原始算法人为经验确定阈值的局限性。实验结果表明:提出的改进Canny算法的红外人脸轮廓提取方法滤波后的图像,相较于原始Canny算法滤波处理,信噪比性能提升了34.40%,结构相似度性能提升了21.66%;最终的红外人脸边缘轮廓提取实验的优质系数值高于对比实验的其他方法,证明改进后的算法对于红外人脸图像边缘轮廓提取具有优越性。
  • 图  1  改进Canny算法的红外人脸图像边缘轮廓提取流程

    Fig.  1  Flow chart of edge contour extraction of infrared face image based on improved Canny algorithm

    图  2  原始Canny算法非极大值抑制插值遍历梯度方向示意

    Fig.  2  Schematic of interpolation gradient direction in non-maximum suppression of original Canny algorithm

    图  3  改进后的非极大值抑制插值遍历梯度方向示意

    Fig.  3  Schematic of improved interpolation gradient direction in non-maximum suppression

    图  4  图像滤波处理效果对比

    Fig.  4  Comparison of image filtering processing effect

    图  5  部分红外人脸图像边缘轮廓提取对比实验结果

    Fig.  5  Experimental results of edge contour extraction comparison from partial infrared face images

    图  6  部分剪裁红外人脸边缘轮廓提取对比实验结果

    Fig.  6  Experimental results of edge contour extraction comparison from partial clipping infrared face images

    图  7  未裁剪图像不同边缘检测算法优质系数对比

    Fig.  7  Comparison of quality coefficients of different edge detection algorithms for uncropped images

    图  8  裁剪图像不同边缘检测算法优质系数对比

    Fig.  8  Comparison of quality coefficients of different edge detection algorithms for clipping images

    表  1  Canny算法边缘检测流程

    Table  1  Edge detection process by Canny algorithm

    步骤操作
    Step1高斯滤波器平滑图像滤除噪声
    Step2计算梯度强度和方向
    Step3非极大值抑制消除杂散响应
    Step4双阈值检测确定真实和潜在边缘
    Step5抑制孤立弱边缘完成边缘检测
    下载: 导出CSV

    表  2  测试图像滤波结果PSNR对比

    Table  2  PSNR comparison of test image filtering results dB

    算法
    图像
    高斯
    滤波
    中值
    滤波
    双边
    滤波
    引导
    滤波
    改进的
    引导滤波
    红外图像118.223220.568421.317622.025924.2705
    剪裁图像120.568423.315825.631527.016031.0563
    红外图像219.031221.165723.028724.963827.8652
    剪裁图像221.235725.064226.916328.215733.1066
    Lena图像24.130632.382335.568636.625240.1549
    下载: 导出CSV

    表  3  测试图像滤波结果SSIM对比

    Table  3  SSIM comparison of test image filtering results %

    算法
    图像
    高斯
    滤波
    中值
    滤波
    双边
    滤波
    引导
    滤波
    改进的
    引导滤波
    红外图像156.8958.3760.5263.8669.01
    剪裁图像160.3661.9363.0465.2373.15
    红外图像258.6259.3861.0765.0471.52
    剪裁图像262.0764.5965.7668.3175.23
    Lena图像68.0268.9471.2375.3581.87
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-04-15
  • 修回日期:  2022-06-06
  • 网络出版日期:  2022-09-23
  • 刊出日期:  2023-01-17

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