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非盲图像复原综述

杨航

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杨航. 非盲图像复原综述[J]. , 2022, 15(5): 954-972. doi: 10.37188/CO.2022-0099
引用本文: 杨航. 非盲图像复原综述[J]. , 2022, 15(5): 954-972.doi:10.37188/CO.2022-0099
YANG Hang. Survey of non-blind image restoration[J]. Chinese Optics, 2022, 15(5): 954-972. doi: 10.37188/CO.2022-0099
Citation: YANG Hang. Survey of non-blind image restoration[J].Chinese Optics, 2022, 15(5): 954-972.doi:10.37188/CO.2022-0099

非盲图像复原综述

doi:10.37188/CO.2022-0099
基金项目:中国科学院青年创新促进会(No. 2020220)
详细信息
    作者简介:

    杨 航(1985—),男,吉林农安人,博士,副研究员。2012年于吉林大学获得理学博士学位,2016年至今为中国科学院长春光学精密机械与物理研究所副研究员。主要从事图像复原、图像增强和目标识别与跟踪方面的研究。E-mail:yanghang@ciomp.ac.cn

  • 中图分类号:TP391

Survey of non-blind image restoration

Funds:Supported by Youth Innovation Promotion Association, CAS (No. 2020220)
More Information
  • 摘要:

    非盲图像复原在数学上是一种典型的病态问题,也是计算机视觉领域的重要研究内容之一,其目标是在点扩散函数已知的情况下,由模糊图像估计出清晰图像,其研究重点是在改善图像清晰度和抑制噪声之间做出适当的折衷。 近50年来,非盲图像复原取得了长足的发展,从早期的维纳滤波到当前的深度学习,学者们提出了数以百计的非盲图像复原算法,并应用在各个领域。本文首先介绍非盲图像复原的基本概念和研究意义,然后依据算法的属性对非盲图像复原算法进行分类概括,从总体上将其分为传统方法和深度学习方法,又进一步将传统方法细分为直接法和迭代法,并依据不同算法的模型特征,分析不同类别中主要算法的优缺点,同时结合多种典型实验,比较分析了一些代表性算法的复原性能,最后展望了非盲图像复原算法的发展趋势,归纳了重点研究方向。

  • 图 1线性时不变系统示意图

    Figure 1.Diagram of linear time invariant system

    图 2ForWaRD算法流程图

    Figure 2.Flow chart of ForWaRD algorithm

    图 3基于BM3D的图像复原方法流程图[31]

    Figure 3.Flow chart of image restoration method based on BM3D[31]

    图 4非盲图像复原算法中学习到的字典[68]。(a)Barbara图像复原局部图;(b)学习到的字典。

    Figure 4.Learned dictionary from non-blind image restoration algorithm[68]. (a) Partial restoration image for Barbara image; (b) the learned dictionary

    图 5组建构的图解[71]

    Figure 5.Illustration of group construction[71]

    图 6文献[93]中使用的去噪网络结构

    Figure 6.Denoising Network structure[93]

    图 7基于CV-CNN网络的图像复原框架[97]

    Figure 7.The image restoration framework based on CV-CNN network[97]

    图 8Vasu等人提出的网络结构[115]

    Figure 8.The network structure proposed by Vasu[115]

    表 1实验设置

    Table 1.Experimental settings

    序号 点扩散函数 噪声水平 图像
    1 9 × 9 boxcar BSNR = 40 dB Cameraman
    2 $k(x,y) = 1/({x^2} + {y^2}),x,y = - 7,\cdots,7$ $ {\sigma ^2} = 2 $ Cameraman
    3 $k(x,y) = 1/({x^2} + {y^2}),x,y = - 7,\cdots,7$ $ {\sigma ^2} = 8 $ Cameraman
    4 $k = {[1,4,6,4,1]^{\rm{T}}}[1,4,6,4,1]/256$ $ {\sigma ^2} = 49 $ Lena
    5 Gaussian型点扩散函数,方差为1.6 $ {\sigma ^2} = 2 $ Barbara
    6 Gaussian型点扩散函数,方差为0.4 $ {\sigma ^2} = 64 $ House
    下载: 导出CSV

    表 28种直接法输出ISNR的对比

    Table 2.Comparison of ISNR output by eight methods

    实验
    方法
    1 2 3 4 5 6
    ForWaRD[15] 7.40 6.75 5.07 2.98 0.98 5.52
    ShearDec[21] 7.89 7.55 5.56
    GSM[23] −1.61 6.84 5.29 0.95 5.98
    SV-GSM[24] 7.33 7.45 5.55 1.36 6.02
    LPA-ICI[26] 8.29 7.82 5.98 3.90
    SA-DCT[27] 8.55 8.11 6.33 4.49 1.02 5.96
    SURE-LET[25] 7.84 7.54 5.22 4.42 1.06 4.38
    BM3DDEB[31] 8.34 8.19 6.40 4.81 1.28 7.21
    下载: 导出CSV

    表 3迭代法实验设置

    Table 3.Experimental setup for iterative methods

    序号 点扩散函数 噪声水平
    1 $ k(x,y) = 1/({x^2} + {y^2}),x,y = - 7,\cdots,7 $ ${\sigma ^2} = 2$
    2 $ k(x,y) = 1/({x^2} + {y^2}),x,y = - 7,\cdots,7 $ ${\sigma ^2} = 8 $
    3 9 × 9 boxcar BSNR= 40 dB
    4 $ k = {[1,4,6,4,1]^{\rm{T} } }[1,4,6,4,1]/256 $ ${\sigma ^2} = 49 $
    5 Gaussian型点扩散函数,方差为1.6 ${\sigma ^2} = 2 $
    6 Gaussian型点扩散函数,方差为0.4 ${\sigma ^2} = 64 $
    下载: 导出CSV

    表 4迭代法实验对比 ISNR

    Table 4.Experimental comparison of ISNR (单位:dB)

    实验序号
    1 2 3 4 5 6
    方法 Cameraman
    BM3DDEB[31] 8.19 6.40 8.34 3.34 3.73 4.70
    L0-Abs[62] 7.70 5.55 9.10 2.93 3.49 1.77
    CGMK[36] 7.80 5.49 9.15 2.80 3.54 3.33
    TVMM[34] 7.41 5.17 8.54 2.57 3.36 1.30
    GFD[33] 8.38 6.52 9.73 3.57 4.02 -
    NCSR[70] 8.78 6.69 10.33 3.78 4.60 4.50
    GSR[71] 8.39 6.39 10.08 3.33 3.94 4.76
    IDDBM3D[73] 8.85 7.12 10.45 3.98 4.31 4.89
    LRD[76] 8.90 7.05 10.70 3.99 4.62 4.62
    House
    BM3DDEB[31] 9.32 8.14 10.85 5.13 4.56 7.21
    L0-Abs[62] 8.40 7.12 11.06 4.55 4.80 2.15
    CGMK[36] 8.31 6.97 10.75 4.48 4.97 4.59
    TVMM[34] 7.98 6.57 10.39 4.12 4.54 2.44
    GFD[33] 9.39 7.75 12.02 5.21 5.39
    NCSR[70] 9.96 8.48 13.12 5.81 5.67 6.94
    GSR[71] 10.02 8.56 13.44 6.00 5.95 7.18
    IDDBM3D[73] 9.95 8.55 12.89 5.79 5.74 7.13
    LRD[76] 10.09 8.67 13.49 6.03 6.22 6.74
    Lena
    BM3DDEB[31] 7.95 6.53 7.97 4.81 4.37 6.40
    L0-Abs[62] 6.66 5.71 7.79 4.09 4.22 1.93
    CGMK[36] 6.76 5.37 7.86 3.49 3.93 4.46
    TVMM[34] 6.36 4.98 7.47 3.52 3.61 2.79
    GFD[33] 8.12 6.65 8.97 4.77 4.95 -
    NCSR[70] 8.03 6.54 9.25 4.93 4.86 6.19
    GSR[71] 8.24 6.76 9.43 5.17 4.96 6.57
    IDDBM3D[73] 7.97 6.61 8.91 4.97 4.85 6.34
    LRD[76] 8.25 6.78 9.31 5.13 5.08 6.13
    Barbara
    BM3DDEB[31] 7.80 3.94 5.86 1.90 1.28 5.80
    L0-Abs[62] 3.51 1.53 3.98 0.73 0.81 1.17
    CGMK[36] 2.45 1.34 3.55 0.44 0.81 0.38
    TVMM[34] 3.10 1.33 3.49 0.41 0.75 0.59
    NCSR 7.76 3.64 5.92 2.06 1.43 5.50
    GSR[71] 8.98 4.80 7.15 2.19 1.58 6.20
    IDDBM3D[73] 7.64 3.96 6.05 1.88 1.16 5.45
    LRD[76] 8.31 5.17 6.95 2.34 1.70 5.37
    下载: 导出CSV

    表 5深度学习方法的实验对比

    Table 5.Experimental comparison of deep learning of different methods

    Levin[106] Sun[107] Martin[108]
    σ 1% 3% 5% 1% 5% 1% 5%
    EPLL[82] 34.06 29.09 26.54 32.48 26.78 29.81 24.66
    0.9310 0.8460 0.7785 0.8815 0.6975 0.8383 0.6276
    CSF[84] 31.09 28.01 26.32 31.52 26.62 29.00 24.93
    0.9024 0.8013 0.7427 0.8622 0.6735 0.8230 0.6428
    MLP[89] 32.08 27.00 25.38 31.47 24.65 28.47 24.01
    0.8884 0.7016 0.6330 0.8535 0.5198 0.7977 0.5619
    LDT[109] 31.53 28.39 26.70 30.52 26.71 28.20 24.90
    0.8977 0.8052 0.7468 0.8399 0.6694 0.7922 0.6358
    FCN[94] 33.22 29.49 27.72 32.36 27.67 29.51 25.45
    0.9267 0.8599 0.8142 0.8853 0.7340 0.8339 0.6771
    IRCNN[93] 34.33 30.04 28.51 33.57 27.64 30.63 25.65
    0.9210 0.8156 0.7762 0.8977 0.6884 0.8645 0.6640
    FDN[87] 34.05 29.77 27.94 32.63 27.75 29.93 25.93
    0.9335 0.8583 0.8139 0.8887 0.7319 0.8555 0.6943
    FNBD[88] 34.81 30.63 27.93 31.22 27.63 30.92 25.49
    0.9398 0.8658 0.7759 0.8860 0.7010 0.8799 0.6589
    RGDN[92] 33.96 29.71 27.45 31.25 26.93 29.51 25.33
    0.9395 0.8662 0.7889 0.8869 0.7161 0.8616 0.6688
    VEM[99] 34.31 30.50 28.52 32.73 29.41
    0.9382 0.8798 0.8348 0.8952 0.8055
    DWDN[101] 36.90 32.77 30.77 34.05 31.74
    0.9614 0.9179 0.8857 0.9225 0.8938
    CV-CNN[97] 35.44 30.85 28.80 33.10 29.54
    0.9467 0.8829 0.8381 0.9022 0.8094
    SVMAP[110] 34.51 29.20 31.89 27.25
    0.9273 0.7940 0.8973 0.7550
    下载: 导出CSV
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  • 收稿日期:2022-05-16
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