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奇异值分解域差异性度量的低景深图像显著性目标提取方法

章秀华 程鉴 洪汉玉 张天序

章秀华, 程鉴, 洪汉玉, 张天序. 奇异值分解域差异性度量的低景深图像显著性目标提取方法[J]. 电子与信息学报, 2022, 44(11): 3987-3997. doi: 10.11999/JEIT210854
引用本文: 章秀华, 程鉴, 洪汉玉, 张天序. 奇异值分解域差异性度量的低景深图像显著性目标提取方法[J]. 电子与信息学报, 2022, 44(11): 3987-3997. doi: 10.11999/JEIT210854
ZHANG Xiuhua, CHENG Jian, HONG Hanyu, ZHANG Tianxu. Salient Target Extraction from Low Depth of Field Images Based on Diversity Measure in Singular Value Decomposition Domain[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3987-3997. doi: 10.11999/JEIT210854
Citation: ZHANG Xiuhua, CHENG Jian, HONG Hanyu, ZHANG Tianxu. Salient Target Extraction from Low Depth of Field Images Based on Diversity Measure in Singular Value Decomposition Domain[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3987-3997. doi: 10.11999/JEIT210854

奇异值分解域差异性度量的低景深图像显著性目标提取方法

doi: 10.11999/JEIT210854
基金项目: 国家自然科学基金(62171329, 61671337)
详细信息
    作者简介:

    章秀华:女,博士,副教授,研究方向为光电信息处理、机器视觉、光学三维测量

    程鉴:男,硕士生,研究方向为机器视觉检测

    洪汉玉:男,博士生导师,教授,研究方向为模式识别与智能系统、机器视觉、光学三维测量

    张天序:男,博士生导师,教授,研究方向为模式识别与智能系统、多谱图像处理

    通讯作者:

    章秀华 amyyzxh@sina.com

  • 中图分类号: TN911.73; TP391.4

Salient Target Extraction from Low Depth of Field Images Based on Diversity Measure in Singular Value Decomposition Domain

Funds: The National Natural Science Foundation of China (62171329, 61671337)
  • 摘要: 针对低景深图像(DOF)目标提取过程中,容易出现目标提取不完整或背景被误识为目标等现象,该文提出一种奇异值分解 (SVD)域差异性度量的低景深图像目标提取方法。先对低景深图像进行高斯模糊,以图像中每一个像素点为中心,利用滑动窗口分别截取模糊前后图像上相同位置的图像块并进行奇异值分解,再构造两奇异值之间的差异特征向量,针对此向量定义低中高全频段信息加权的差异性度量算子,计算对应像素点的显著性特征值,逐像素处理得到显著性结果图并进行阈值化处理,实现低景深图像目标的有效提取。对大量低景深图像进行处理,并与几种现有方法进行比较,提出方法的F度量值最大可提高54%,平均绝对误差减少76%~87%,可完整提取目标并有效去除背景,具有较强的可靠性。
  • 图  1  低景深图像中离焦模糊区域A和清晰目标区域B

    图  2  不同图像块的SVD奇异值分布

    图  3  模糊后不同图像块的SVD奇异值分布曲线

    图  4  奇异值分布曲线及其虚线框内局部放大图

    图  5  不同图像块的差异特征分布曲线

    图  6  单个像素特征强度计算流程示意图

    图  7  模糊因子取不同值时的特征显著性强度图

    图  8  不同模糊因子$ \sigma $对应的PM值曲线

    图  9  简单背景图像的不同方法处理结果

    图  10  复杂背景图像的不同方法处理结果

    图  11  区域分类示意图

    图  12  不同方法的PR曲线结果图

    表  1  像素特征度量值计算

     (1) 输入低景深图像$ f(x,y) $
     (2) 对$ f(x,y) $进行高斯模糊处理得到图像${f_{{\rm{blur}}} }(x,y)$
     (3) x=1:M 循环
     (4) y=1:N 循环
     (5) 式(5)、式(6),计算以(x,y)为中心的窗口大小图像块
       $ {f^k}(x,y) $和$f_{{\rm{blur}}}^k(x,y)$的SVD奇异值
     (6) 式(7), 计算两图像块奇异值差异特征向量$ {{\mathbf{\delta }}_\lambda } $
     (7) 利用度量式(8), 计算(x,y)像素点的特征度量值
     (8) 终止循环(4)
     (9) 终止循环(3)
     (10) 利用式(9)进行阈值化,得到目标提取结果图像
    下载: 导出CSV

    表  2  不同方法的F-measure值

    LCFTACHCSR文献[20]SVD-DM
    F-measure0.58670.60690.55880.49550.70940.89420.7639
    下载: 导出CSV

    表  3  不同方法对应的平均绝对误差值

    LCFTACHCSR文献[20]SVD-DM
    MAE0.64150.69930.73200.66290.39830.07280.0957
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-08-18
  • 录用日期:  2021-12-13
  • 修回日期:  2021-12-10
  • 网络出版日期:  2021-12-26
  • 刊出日期:  2022-11-14

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