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基于多通道颜色判别的深度图像空洞渐进式填充算法

章秀华 史鹏博 洪汉玉 张天序

章秀华, 史鹏博, 洪汉玉, 张天序. 基于多通道颜色判别的深度图像空洞渐进式填充算法[J]. 微电子学与计算机, 2022, 39(4): 58-64. doi: 10.19304/J.ISSN1000-7180.2021.1045
引用本文: 章秀华, 史鹏博, 洪汉玉, 张天序. 基于多通道颜色判别的深度图像空洞渐进式填充算法[J]. 微电子学与计算机, 2022, 39(4): 58-64. doi: 10.19304/J.ISSN1000-7180.2021.1045
ZHANG Xiuhua, SHI Pengbo, HONG Hanyu, ZHANG Tianxu. A progressive filling algorithm for depth image holes based on multi-channel color discrimination[J]. Microelectronics & Computer, 2022, 39(4): 58-64. doi: 10.19304/J.ISSN1000-7180.2021.1045
Citation: ZHANG Xiuhua, SHI Pengbo, HONG Hanyu, ZHANG Tianxu. A progressive filling algorithm for depth image holes based on multi-channel color discrimination[J]. Microelectronics & Computer, 2022, 39(4): 58-64. doi: 10.19304/J.ISSN1000-7180.2021.1045

基于多通道颜色判别的深度图像空洞渐进式填充算法

doi: 10.19304/J.ISSN1000-7180.2021.1045
基金项目: 

国家自然科学基金资助项目 61671337

湖北省教育厅科学研究计划重点项目 D20181508

详细信息
    作者简介:

    章秀华  女,(1976-),博士,副教授.研究方向为光电探测与信息处理、机器视觉、光学三维测量

    史鹏博  男,(1996-),硕士研究生.研究方向为图像处理、机器视觉及应用

    张天序  男,(1947-),教授,博士生导师.研究方向为自动目标识别、计算机视觉与图像分析、精确制导、模式识别理论与应用

    通讯作者:

    洪汉玉(通讯作者)  男,(1964-),教授,博士生导师.研究方向为现代图像处理、模式识别、智能控制系统、机器视觉、三维非接触视觉检测.E-mail:hhyhong@163.com

  • 中图分类号: TP391.413

A progressive filling algorithm for depth image holes based on multi-channel color discrimination

  • 摘要:

    深度图像在自动驾驶、三维测量等领域发挥着越来越重要的作用,针对深度图像中空洞信息难以准确修复,填充速率较慢等问题,本文提出一种基于多通道颜色判别的深度图像空洞渐进式填充算法.首先根据深度图像和彩色图像设置筛选条件,对空洞点邻域内像素进行准确筛选,然后计算邻域内像素在空间域和值域下的双边权值并得到带有权重的二维填充模板,进行填充时将二维模板化简为两个互相垂直的一维模板以提高填充速度并采用渐进式填充方法对空洞进行填充.在公开数据集上对实验结果在主观视觉上进行定性对比分析,客观上通过均方根误差和峰值信噪比两个评价指标对本文算法处理效果进行准确分析.实验结果表明,本文方法能较好地保留物体的边界信息,有效防止填充后物体边缘模糊的现象,填充结果准确,填充速率得到优化.

     

  • 图 1  彩色图像3*3邻域示意图

    Figure 1.  Schematic diagram of 3*3 neighborhood of color image

    图 2  渐进填充过程图

    Figure 2.  Progressive filling process diagram

    图 3  算法流程图

    Figure 3.  Algorithm flow chart

    图 4  小目标物体修复结果图

    Figure 4.  The result of repairing small target objects

    图 5  小目标物体边界局部采样放大对比

    Figure 5.  Comparison of local sampling and zooming in the boundary of small target objects

    图 6  室内场景图修复结果图

    Figure 6.  Repair result of indoor scene graph

    图 7  室内场景图边界局部采样放大对比

    Figure 7.  Enlarged comparison of local sampling at the boundary of indoor scene graph

    图 8  不同场景下的RMSE值对比

    Figure 8.  Comparison of RMSE values in different scenarios

    图 9  两种场景下不同算法的PSNR值对比

    Figure 9.  Comparison of PSNR values of different algorithms in two scenarios

    表  1  不同滤波算法运算时间比较(ms/帧)

    Table  1.   Comparison of operation time of different filtering algorithms (ms/frame)

    算法 小目标图像 室内场景图像
    GF 150 927
    BF 160 930
    PFMD 110 520
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-08-20
  • 修回日期:  2021-09-19
  • 网络出版日期:  2022-05-12

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