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基于结构字典学习的图像复原方法

杨航 吴笑天 王宇庆

杨航, 吴笑天, 王宇庆. 基于结构字典学习的图像复原方法[J]. 中国光学(中英文), 2017, 10(2): 207-218. doi: 10.3788/CO.20171002.0207
引用本文: 杨航, 吴笑天, 王宇庆. 基于结构字典学习的图像复原方法[J]. 中国光学(中英文), 2017, 10(2): 207-218. doi: 10.3788/CO.20171002.0207
YANG Hang, WU Xiao-tian, WANG Yu-qing. Image restoration approach based on structure dictionary learning[J]. Chinese Optics, 2017, 10(2): 207-218. doi: 10.3788/CO.20171002.0207
Citation: YANG Hang, WU Xiao-tian, WANG Yu-qing. Image restoration approach based on structure dictionary learning[J]. Chinese Optics, 2017, 10(2): 207-218. doi: 10.3788/CO.20171002.0207

基于结构字典学习的图像复原方法

doi: 10.3788/CO.20171002.0207
基金项目: 

国家自然科学基金资助项目 61401425

详细信息
    通讯作者:

    杨航 (1985-), 男, 吉林长春人, 博士, 助理研究员, 20012年于吉林大学获得博士学位, 主要从事机器视觉及图像复原方面的研究。E-mail:yhang3109@163.com

  • 中图分类号: TP391

Image restoration approach based on structure dictionary learning

Funds: 

National Natural Science Foundation of China 61401425

  • 摘要: 本文提出一种新的结构字典学习方法,并利用它进行图像复原。首先给出结构字典学习的基本内容和方法,然后将傅里叶正则化方法和结构字典学习方法有效整合到图像复原算法中。结构字典学习方法是先将原图像进行结构分解,再分别学习出每个结构图像中的字典,最后利用这些字典对原图像进行稀疏的表示。结合傅里叶正则化,提出了一种有效的迭代图像复原算法:第一步在傅里叶域利用正则化反卷积方法得到图像的初步估计;第二步采用结构字典学习的方法对遗留的噪声进行去噪处理。实验结果表明,提出的方法在改进信噪比和视觉质量上都要优于6种先进的图像复原方法,改进的信噪比平均提升0.5 dB以上。

     

  • 图 1  卷帘导引滤波结构分解效果图

    Figure 1.  Results of rolling guidance filter

    图 2  Exp2视觉效果对比图

    Figure 2.  Visual comparison of House image in Exp2

    图 3  Exp3视觉效果对比图

    Figure 3.  Details of the image deconvolution experiment with a Lena in Exp3

    图 4  Exp5视觉效果对比图

    Figure 4.  Details of the image deconvolution experiment with a Barbara in Exp5

    图 5  Exp7实验效果对比图

    Figure 5.  Details of the image deconvolution experiment with a Barbara in Exp7

    表  1  本文算法与现今主流算法ISNR值对比

    Table  1.   Output ISNR (dB) comparison of several state-of-the-art and the proposed deconvolution algorithms

    Methods Exp1 Exp2 Exp3 Ex4 Exp5 Exp6 Exp7
    ours 12.31 5.29 8.55 4.79 2.41 1.72 6.02
    ForWaRD 9.56 3.85 6.97 3.50 0.94 0.98 4.02
    FTVd 10.39 4.65 7.47 3.61 0.63 0.75 3.57
    L0-ABS 11.06 4.80 7.79 4.22 0.73 0.81 3.98
    SURE-LET 10.72 4.26 7.96 4.25 1.13 1.06 4.24
    BSDL 6.99 4.88 4.83 4.50 2.00 1.13 2.65
    BM3DDEB 10.85 4.56 7.97 4.37 1.90 1.28 5.86
    下载: 导出CSV

    表  2  本文算法与现今主流算法实验运行时间 (秒) 对比

    Table  2.   Running time comparison of several state-of-the-art and the proposed deconvolution algorithm (second)

    Methods 256×256 512×512
    ours 31.20 130.64
    ForWaRD 1.21 5.12
    FTVd 0.63 3.25
    L0-ABS 7.71 32.70
    SURE-LET 0.33 1.43
    BSDL 60.29 263.86
    BM3DDEB 0.41 1.67
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-10-12
  • 修回日期:  2016-12-05
  • 刊出日期:  2017-04-01

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