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结合相机阵列选择性光场重聚焦的显著性检测

冯洁 王世刚 韦健 赵岩

冯洁, 王世刚, 韦健, 赵岩. 结合相机阵列选择性光场重聚焦的显著性检测[J]. 中国光学. doi: 10.37188/CO.2020-0165
引用本文: 冯洁, 王世刚, 韦健, 赵岩. 结合相机阵列选择性光场重聚焦的显著性检测[J]. 中国光学. doi: 10.37188/CO.2020-0165
FENG Jie, WANG Shi-gang, WEI Jian, ZHAO Yan. Saliency detection combined with selective light field refocusing of camera array[J]. Chinese Optics. doi: 10.37188/CO.2020-0165
Citation: FENG Jie, WANG Shi-gang, WEI Jian, ZHAO Yan. Saliency detection combined with selective light field refocusing of camera array[J]. Chinese Optics. doi: 10.37188/CO.2020-0165

结合相机阵列选择性光场重聚焦的显著性检测

doi: 10.37188/CO.2020-0165
基金项目: 国家自然基金重点项目(No. 61631009);国家十三五重点研发计划项目(No. 2017YFB0404800);中央高校基本科研业务费专项资金(No. 2017TD-19)
详细信息
    作者简介:

    冯洁:冯 洁(1995—),女,内蒙古乌兰察布人,硕士研究生,2018年于吉林大学获得学士学位,主要从事光场图像处理方面的研究。E-mail: fengjie18@ mails.jlu.edu.cn

    王世刚(1961—),男,吉林长春人,教授,博士生导师,1983年于东北大学获得学士学位,1997年于吉林工业大学获得硕士学位,2001年于吉林大学获得博士学位,主要从事图像与视频信号智能处理方面的研究。E-mail: wangshigang@vip.sina.com

  • 中图分类号: TP391.4

Saliency detection combined with selective light field refocusing of camera array

Funds: Supported by National Natural Science Foundation of China (No. 61631009); National Key Research and Development Plan of 13th Five-year (No. 2017YFB0404800); Fundamental Research Funds for the Central Universities (No. 2017TD-19)
More Information
  • 摘要: 针对现有方法处理包含多个显著目标以及显著目标的某些区域与背景区域对比不明显的场景所得显著图不够精细,甚至会丢失某些显著性区域的不足,本文提出了一种结合相机阵列选择性光场重聚焦的显著性检测方法。选用光场数据集,利用同一场景的多幅视点图像,首先对中心视点图像进行结合超分辨率的重聚焦渲染;然后在基于图的显著性检测方法的基础上(基于图的显著性检测方法是一类经典的显著性检测方法,该类算法通过构建图模型进行显著性检测,因此得名基于图的显著性检测算法,本文是在此基础上进行改进,提出新的算法)提出结合全局和局部平滑度约束的传播模型以防止错误标签传播,得到的显著性粗图经过目标图的细化后最终输出精细的检测结果。另外,对于包含多个显著目标的场景,通过选择对场景中某一深度层进行重聚焦,同时对其他深度层产生不同程度的模糊,可以更精确、细致地检测出位于该深度层上的显著目标,一定程度上实现了可选择的显著性检测。在4D光场数据集上进行了实验,结果表明:本文提出的方法所得显著图与真值图之间的平均绝对误差的均值为0.212 8,较现有方法有所降低,检测结果包含更丰富的显著性目标信息,改善了现有显著性检测方法的不足。
  • 图  1  结合相机阵列选择性光场重聚焦的显著性检测算法的框架图

    Figure  1.  Framework diagram of the saliency detection algorithm combined with selective light field refocusing of camera array

    图  2  聚焦于场景不同深度层上的重聚焦结果

    Figure  2.  Refocusing results focusing on different depth layers of the scene

    图  3  5种算法对场景Table和Boxs进行显著性检测所得结果比较

    Figure  3.  Comparison of the saliency detection results obtained by five algorithms for the scene Table and Boxs

    图  4  本文算法所得结果与最新的基于深度学习的显著性检测算法所得结果的比较

    Figure  4.  The comparison between the results of our algorithm and the latest saliency detection algorithm based on deep learning

    图  5  聚焦于场景不同深度层所得显著图比较

    Figure  5.  Comparison of saliency maps obtained by focusing on different depth layers of the scene

    表  1  5种算法的平均MAE值

    Table  1.   Average MAE values of 5 different kinds of algorithms

    AlgorithmOursRef. [19]Ref. [18]Ref. [9]Ref. [13]
    Average MAE0.21280.24770.42570.34360.5617
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-09-08
  • 修回日期:  2020-09-17
  • 网络出版日期:  2021-02-22

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