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非盲图像复原综述

杨航

杨航. 非盲图像复原综述[J]. 中国光学(中英文), 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]. 中国光学(中英文), 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 2  ForWaRD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 8  Vasu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
    19 × 9 boxcarBSNR = 40 dBCameraman
    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 $Lena
    5Gaussian型点扩散函数,方差为1.6$ {\sigma ^2} = 2 $Barbara
    6Gaussian型点扩散函数,方差为0.4$ {\sigma ^2} = 64 $House
    下载: 导出CSV

    表  2  8种直接法输出ISNR的对比

    Table  2.   Comparison of ISNR output by eight methods

    实验
    方法
    123456
    ForWaRD[15]7.406.755.072.980.985.52
    ShearDec[21]7.897.555.56
    GSM[23]−1.616.845.290.955.98
    SV-GSM[24]7.337.455.551.366.02
    LPA-ICI[26]8.297.825.983.90
    SA-DCT[27]8.558.116.334.491.025.96
    SURE-LET[25]7.847.545.224.421.064.38
    BM3DDEB[31]8.348.196.404.811.287.21
    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 $
    39 × 9 boxcarBSNR = 40 dB
    4$ k = {[1,4,6,4,1]^{\rm{T} } }[1,4,6,4,1]/256 $${\sigma ^2} = 49 $
    5Gaussian型点扩散函数,方差为1.6${\sigma ^2} = 2 $
    6Gaussian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
    123456
    方法Cameraman
    BM3DDEB[31]8.196.408.343.343.734.70
    L0-Abs[62]7.705.559.102.933.491.77
    CGMK[36]7.805.499.152.803.543.33
    TVMM[34]7.415.178.542.573.361.30
    GFD[33]8.386.529.733.574.02-
    NCSR[70]8.786.6910.333.784.604.50
    GSR[71]8.396.3910.083.333.944.76
    IDDBM3D[73]8.857.1210.453.984.314.89
    LRD[76]8.907.0510.703.994.624.62
    House
    BM3DDEB[31]9.328.1410.855.134.567.21
    L0-Abs[62]8.407.1211.064.554.802.15
    CGMK[36]8.316.9710.754.484.974.59
    TVMM[34]7.986.5710.394.124.542.44
    GFD[33]9.397.7512.025.215.39
    NCSR[70]9.968.4813.125.815.676.94
    GSR[71]10.028.5613.446.005.957.18
    IDDBM3D[73]9.958.5512.895.795.747.13
    LRD[76]10.098.6713.496.036.226.74
    Lena
    BM3DDEB[31]7.956.537.974.814.376.40
    L0-Abs[62]6.665.717.794.094.221.93
    CGMK[36]6.765.377.863.493.934.46
    TVMM[34]6.364.987.473.523.612.79
    GFD[33]8.126.658.974.774.95-
    NCSR[70]8.036.549.254.934.866.19
    GSR[71]8.246.769.435.174.966.57
    IDDBM3D[73]7.976.618.914.974.856.34
    LRD[76]8.256.789.315.135.086.13
    Barbara
    BM3DDEB[31]7.803.945.861.901.285.80
    L0-Abs[62]3.511.533.980.730.811.17
    CGMK[36]2.451.343.550.440.810.38
    TVMM[34]3.101.333.490.410.750.59
    NCSR7.763.645.922.061.435.50
    GSR[71]8.984.807.152.191.586.20
    IDDBM3D[73]7.643.966.051.881.165.45
    LRD[76]8.315.176.952.341.705.37
    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]
    σ1%3%5%1%5%1%5%
    EPLL[82]34.0629.0926.5432.4826.7829.8124.66
    0.93100.84600.77850.88150.69750.83830.6276
    CSF[84]31.0928.0126.3231.5226.6229.0024.93
    0.90240.80130.74270.86220.67350.82300.6428
    MLP[89]32.0827.0025.3831.4724.6528.4724.01
    0.88840.70160.63300.85350.51980.79770.5619
    LDT[109]31.5328.3926.7030.5226.7128.2024.90
    0.89770.80520.74680.83990.66940.79220.6358
    FCN[94]33.2229.4927.7232.3627.6729.5125.45
    0.92670.85990.81420.88530.73400.83390.6771
    IRCNN[93]34.3330.0428.5133.5727.6430.6325.65
    0.92100.81560.77620.89770.68840.86450.6640
    FDN[87]34.0529.7727.9432.6327.7529.9325.93
    0.93350.85830.81390.88870.73190.85550.6943
    FNBD[88]34.8130.6327.9331.2227.6330.9225.49
    0.93980.86580.77590.88600.70100.87990.6589
    RGDN[92]33.9629.7127.4531.2526.9329.5125.33
    0.93950.86620.78890.88690.71610.86160.6688
    VEM[99]34.3130.5028.5232.7329.41
    0.93820.87980.83480.89520.8055
    DWDN[101]36.9032.7730.7734.0531.74
    0.96140.91790.88570.92250.8938
    CV-CNN[97]35.4430.8528.8033.1029.54
    0.94670.88290.83810.90220.8094
    SVMAP[110]34.5129.2031.8927.25
    0.92730.79400.89730.7550
    下载: 导出CSV
  • [1] STARCK J L, PANTIN E, MURTAGH F. Deconvolution in astronomy: a review[J]. Publications of the Astronomical Society of the Pacific, 2002, 114(800): 1051-1069. doi: 10.1086/342606
    [2] JAIN A K. Fundamentals of Digital Image Processing[M]. Upper Saddle River: Prentice-Hall, 1989: 1420-1424.
    [3] 沈峘, 李舜酩, 毛建国, 等. 数字图像复原技术综述[J]. 中国图像图形学报,2009,14(9):1764-1775.

    SHEN H, LI SH M, MAO J G, et al. Digital image restoration techniques: a review[J]. Journal of Image and Graphics, 2009, 14(9): 1764-1775. (in Chinese)
    [4] 闫敬文, 彭鸿, 刘蕾, 等. 基于L0正则化模糊核估计的遥感图像复原[J]. 光学 精密工程,2014,22(9):2572-2579. doi: 10.3788/OPE.20142209.2572

    YAN J W, PENG H, LIU L, et al. Remote sensing image restoration based on zero-norm regularized kernel estimation[J]. Optics and Precision Engineering, 2014, 22(9): 2572-2579. (in Chinese) doi: 10.3788/OPE.20142209.2572
    [5] 李东升, 陈春晓, 王章立, 等. 基于全局方差和噪声估计的维纳滤波图像的复原方法[J]. 生物医学工程研究,2017,36(4):331-335. doi: 10.19529/j.cnki.1672-6278.2017.04.11

    LI D SH, CHEN CH X, WANG ZH L, et al. Wiener filter image restoration based on global variance and noise estimation[J]. Journal of Biomedical Engineering Research, 2017, 36(4): 331-335. (in Chinese) doi: 10.19529/j.cnki.1672-6278.2017.04.11
    [6] 朱非甲, 金鹏. 面向工业检测的图像快速去直线运动模糊方法[J]. 哈尔滨工业大学学报,2018,50(9):123-129. doi: 10.11918/j.issn.0367-6234.201704118

    ZHU F J, JIN P. Fast moving line motion de-blurring for image detection of industrial inspection[J]. Journal of Harbin Institute of Technology, 2018, 50(9): 123-129. (in Chinese) doi: 10.11918/j.issn.0367-6234.201704118
    [7] 陈灏. 光学稀疏孔径成像系统图像恢复算法研究[D]. 杭州: 浙江大学, 2017.

    CHEN H. Image restoration algorithm for optical sparse aperture systems[D]. Hangzhou: Zhejiang University, 2017. (in Chinese)
    [8] 杨航. 图像反卷积算法研究[D]. 长春: 吉林大学, 2012: 3-7.

    YANG H. The study on image deconvolution algorithm[D]. Changsha: Jilin University, 2012: 3-7. (in Chinese)
    [9] HANSEN P C. Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion[M]. Philadelphia: SIAM, 1997.
    [10] FAN J Q, KOO J. Wavelet deconvolution[J]. IEEE Transactions on Information Theory, 2002, 48(3): 734-747. doi: 10.1109/18.986021
    [11] JOHNSTONE I M, KERKYACHARIAN G, PICARD D, et al. Wavelet deconvolution in a periodic setting[J]. Journal of the Royal Statistical Society. Series B, 2004, 66(3): 547-573. doi: 10.1111/j.1467-9868.2004.02056.x
    [12] PENSKY M, VIDAKOVIC B. Adaptive wavelet estimator for nonparametric density deconvolution[J]. Annals of Statistics, 1999, 27(6): 2033-2053.
    [13] DONOHO D L. Nonlinear solution of linear inverse problems by wavelet–vaguelette decomposition[J]. Applied and Computational Harmonic Analysis, 1995, 2(2): 101-126. doi: 10.1006/acha.1995.1008
    [14] KALIFA J, MALLAT S, ROUGE B. Deconvolution by thresholding in mirror wavelet bases[J]. IEEE Transactions on Image Processing, 2003, 12(4): 446-457. doi: 10.1109/TIP.2003.810592
    [15] NEELAMANI R, CHOI H, BARANIUK R. ForWaRD: Fourier-wavelet regularized deconvolution for Ill-conditioned systems[J]. IEEE Transactions on Signal Processing, 2004, 52(2): 418-433. doi: 10.1109/TSP.2003.821103
    [16] CANDÈS E, DEMANET L, DONOHO D L, et al. Fast discrete curvelet transforms[J]. Multiscale Modeling &Simulation, 2006, 5(3): 861-899.
    [17] DO M N, VETTERLI M. The contourlet transform: an efficient directional multiresolution image representation[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2091-2106. doi: 10.1109/TIP.2005.859376
    [18] EASLEY G R, LABATE D, LIM W Q. Sparse directional image representations using the discrete shearlet transform[J]. Applied and Computational Harmonic Analysis, 2008, 25(1): 25-46. doi: 10.1016/j.acha.2007.09.003
    [19] DEMANET L, YING L X. Wave atoms and sparsity of oscillatory patterns[J]. Applied and Computational Harmonic Analysis, 2007, 23(3): 368-387. doi: 10.1016/j.acha.2007.03.003
    [20] NEELAMANI R N, DEFFENBAUGH M, BARANIUK R G. Texas two-step: a framework for optimal multi-input single-output deconvolution[J]. IEEE Transactions on Image Processing, 2007, 16(11): 2752-2765. doi: 10.1109/TIP.2007.906251
    [21] PATEL V M, EASLEY G R, HEALY D M. Shearlet-based deconvolution[J]. IEEE Transactions on Image Processing, 2009, 18(12): 2673-2685. doi: 10.1109/TIP.2009.2029594
    [22] YANG H, ZHANG ZH B. Fusion of wave atom-based wiener shrinkage filter and joint non-local means filter for texture-preserving image deconvolution[J]. Optical Engineering, 2012, 51(6): 067009. doi: 10.1117/1.OE.51.6.067009
    [23] PORTILLA J, SIMONCELLI E. Image restoration using Gaussian scale mixtures in the wavelet domain[C]. Proceedings 2003 International Conference on Image Processing, IEEE, 2003: Ⅱ-965.
    [24] GUERRERO-COLON J A, MANCERA L, PORTILLA J. Image restoration using space-variant Gaussian scale mixtures in overcomplete pyramids[J]. IEEE Transactions on Image Processing, 2008, 17(1): 27-41. doi: 10.1109/TIP.2007.911473
    [25] XUE F, LUISIER F, BLU T. Multi-wiener SURE-LET deconvolution[J]. IEEE Transactions on Image Processing, 2013, 22(5): 1954-1968. doi: 10.1109/TIP.2013.2240004
    [26] KATKOVNIK V, EGIAZARIAN K O, ASTOLA J. A spatially adaptive nonparametric regression image deblurring[J]. IEEE Transactions on Image Processing, 2005, 14(10): 1469-1478. doi: 10.1109/TIP.2005.851705
    [27] FOI A, DABOV K, KATKOVNIK V, et al. Shape-adaptive DCT for denoising and image reconstruction[J]. Proceedings of SPIE, 2006, 6064: 203-214.
    [28] BUADES A, COLL B, MOREL J. Nonlocal image and movie denoising[J]. International Journal of Computer Vision, 2008, 76(2): 123-139. doi: 10.1007/s11263-007-0052-1
    [29] CHEN F, HUANG X J, CHEN W F. Texture-preserving image deblurring[J]. IEEE Signal Processing Letters, 2010, 17(12): 1018-1021. doi: 10.1109/LSP.2010.2078807
    [30] DABOV K, FOI A, KATKOVNIK V, et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions On Image Processing, 2007, 16(8): 2080-2095.
    [31] DABOVE K, FOI A, KATKOVNIK V, et al. Image restoration by sparse 3D transform-domain collaborative filtering[J]. Proceedings of SPIE, 2008, 6812: 681207. doi: 10.1117/12.766355
    [32] BANHAM M R, KATSAGGELOS A K. Spatially adaptive wavelet-based multiscale image restoration[J]. IEEE Transactions on Image Processing, 1996, 5(4): 619-634. doi: 10.1109/83.491338
    [33] YANG H, ZHANG ZH B, GUAN Y J. An adaptive parameter estimation for guided filter based image deconvolution[J]. Signal Processing, 2017, 138: 16-26. doi: 10.1016/j.sigpro.2017.03.006
    [34] WANG Y L, YANG J F, YIN W T, et al. A new alternating minimization algorithm for total variation image reconstruction[J]. SIAM Journal on Imaging Sciences, 2008, 1(3): 248-272. doi: 10.1137/080724265
    [35] CHO S, WANG J, LEE S. Handling outliers in non-blind image deconvolution[C]. Proceedings of 2011 International Conference on Computer Vision, IEEE, 2011: 495-502.
    [36] CHANTAS G, GALATSANOS N P, MOLINA R, et al. Variational Bayesian image restoration with a product of spatially weighted total variation image priors[J]. IEEE Transactions on Image Processing, 2010, 19(2): 351-362. doi: 10.1109/TIP.2009.2033398
    [37] WEN Y W, NG M K, CHING W K. Iterative algorithms based on decoupling of deblurring and denoising for image restoration[J]. SIAM Journal on Scientific Computing, 2008, 30(5): 2655-2674. doi: 10.1137/070683374
    [38] TAKEDA H, FARSIU S, MILANFAR P. Deblurring using regularized locally adaptive kernel regression[J]. IEEE Transactions on Image Processing, 2008, 17(4): 550-563. doi: 10.1109/TIP.2007.918028
    [39] LUCY L B. An iterative technique for the rectification of observed distributions[J]. Astronomical Journal, 1974, 79: 745. doi: 10.1086/111605
    [40] WHYTE O, SIVIC J, ZISSERMAN A. Deblurring shaken and partially saturated images[C]. Proceedings of 2011 IEEE International Conference on Computer Vision Workshops, IEEE, 2011: 745-752.
    [41] RUDIN L I, OSHER S, FATEMI E. Nonlinear total variation based noise removal algorithms[J]. Physica D:Nonlinear Phenomena, 1992, 60(1-4): 259-268. doi: 10.1016/0167-2789(92)90242-F
    [42] BECK A, TEBOULLE M. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems[J]. IEEE Transactions on Image Processing, 2009, 18(11): 2419-2434. doi: 10.1109/TIP.2009.2028250
    [43] CHAN T F, GOLUB G H, MULET P. A nonlinear primal-dual method for total variation-based image restoration[J]. SIAM Journal on Scientific Computing, 1999, 20(6): 1964-1977. doi: 10.1137/S1064827596299767
    [44] CHEN D Q, ZHANG H, CHENG L ZH. A fast fixed point algorithm for total variation deblurring and segmentation[J]. Journal of Mathematical Imaging and Vision, 2012, 43(3): 167-179. doi: 10.1007/s10851-011-0298-7
    [45] SHI F, CHENG J, WANG L, et al. LRTV: MR image super-resolution with low-rank and total variation regularizations[J]. IEEE Transactions on Medical Imaging, 2015, 34(12): 2459-2466. doi: 10.1109/TMI.2015.2437894
    [46] RUDIN L I, OSHER S. Total variation based image restoration with free local constraints[C]. Proceedings of 1st International Conference on Image Processing, IEEE, 1994: 31-35.
    [47] 童蓓蕾. 基于变分法的图像复原算法研究[D]. 合肥: 中国科学技术大学, 2018.

    TONG B L. Research of image restoration algorithm based on variational method[D]. Hefei: University of Science and Technology of China, 2018. (in Chinese)
    [48] OSHER S, BURGER M, GOLDFARB D, et al. An iterative regularization method for total variation-based image restoration[J]. SIAM Journal on Multiscale Model &Simulation, 2005, 4(2): 460-489.
    [49] GOLDSTEIN T, OSHER S. The split Bregman method for L1-regularized problems[J]. SIAM Journal on Imaging Sciences, 2009, 2(2): 323-343. doi: 10.1137/080725891
    [50] BIOUCAS-DIAS J M, FIGUEIREDO M A T. A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration[J]. IEEE Transactions on Image Processing, 2007, 16(12): 2992-3004. doi: 10.1109/TIP.2007.909319
    [51] MICHAILOVICH O V. An iterative shrinkage approach to total-variation image restoration[J]. IEEE Transactions on Image Processing, 2011, 20(5): 1281-1299. doi: 10.1109/TIP.2010.2090532
    [52] VONESCH C, UNSER M. A fast thresholded landweber algorithm for wavelet-regularized multidimensional deconvolution[J]. IEEE Transactions on Image Processing, 2008, 17(4): 539-549. doi: 10.1109/TIP.2008.917103
    [53] NG M K, WEISS P, YUAN X M. Solving constrained total-variation image restoration and reconstruction problems via alternating direction methods[J]. SIAM Journal on Scientific Computing, 2010, 32(5): 2710-2736. doi: 10.1137/090774823
    [54] OLIVEIRA J P, BIOUCAS-DIAS J M, FIGUEIREDO M A T. Adaptive total variation image deblurring: a majorization-minimization approach[J]. Signal Processing, 2009, 89(9): 1683-1693. doi: 10.1016/j.sigpro.2009.03.018
    [55] KRISHNAN D, FERGUS R. Fast image deconvolution using hyper-Laplacian priors[C]. Proceedings of the 22nd International Conference on Neural Information Processing Systems, Curran Associates Inc. , 2009: 1033-1041.
    [56] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. doi: 10.1109/TIT.2006.871582
    [57] PAN J SH, HU ZH, SU ZH X, et al. L0-regularized intensity and gradient prior for deblurring text images and beyond[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(2): 342-355. doi: 10.1109/TPAMI.2016.2551244
    [58] VONESCH C, UNSER M. A fast iterative thresholding algorithm for wavelet-regularized deconvolution[J]. Proceedings of SPIE, 2007, 6701: 67010D.
    [59] FIGUEIREDO M A T, NOWAK R D. An EM algorithm for wavelet-based image restoration[J]. IEEE Transactions on Image Processing, 2003, 12(8): 906-916. doi: 10.1109/TIP.2003.814255
    [60] DONG B, ZHANG Y. An efficient algorithm for ℓ0 minimization in wavelet frame based image restoration[J]. Journal of Scientific Computing, 2013, 54(2): 350-368.
    [61] CAI J F, DONG B, SHEN Z W. Image restoration: a wavelet frame based model for piecewise smooth functions and beyond[J]. Applied and Computational Harmonic Analysis, 2016, 41(1): 94-138. doi: 10.1016/j.acha.2015.06.009
    [62] PORTILLA J. Image restoration through l0 analysis-based sparse optimization in tight frames[C]. Proceedings of the 16th IEEE International Conference on Image Processing, IEEE, 2009: 3909-3912.
    [63] CAI J F, OSHER S, SHEN Z W. Split Bregman methods and frame based image restoration[J]. Multiscale Modeling &Simulation, 2010, 8(2): 337-369.
    [64] STARCK J L, NGUYEN M K, MURTAGH F. Wavelets and curvelets for image deconvolution: a combined approach[J]. Signal Processing, 2003, 83(10): 2279-2283. doi: 10.1016/S0165-1684(03)00150-6
    [65] LV X G, SONG Y ZH, LI F. An efficient nonconvex regularization for wavelet frame and total variation based image restoration[J]. Journal of Computational and Applied Mathematics, 2015, 290: 553-566. doi: 10.1016/j.cam.2015.06.006
    [66] ELAD M, AHARON M. Image denoising via sparse and redundant representations over learned dictionaries[J]. IEEE Transactions on Image Processing, 2006, 15(12): 3736-3745. doi: 10.1109/TIP.2006.881969
    [67] AHARON M, ELAD M, BRUCKSTEIN A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322. doi: 10.1109/TSP.2006.881199
    [68] YANG H, ZHU M, WU X T, et al. Dictionary learning approach for image deconvolution with variance estimation[J]. Applied Optics, 2014, 53(25): 5677-5684. doi: 10.1364/AO.53.005677
    [69] DONG W SH, ZHANG L, SHI G M. Centralized sparse representation for image restoration[C]. Proceedings of 2011 International Conference on Computer Vision, IEEE, 2011: 1259-1266.
    [70] DONG W SH, ZHANG L, SHI G M, et al. Nonlocally centralized sparse representation for image restoration[J]. IEEE Transactions on Image Processing, 2013, 22(4): 1620-1630. doi: 10.1109/TIP.2012.2235847
    [71] ZHANG J, ZHAO D B, GAO W. Group-based sparse representation for image restoration[J]. IEEE Transactions on Image Processing, 2014, 23(8): 3336-3351. doi: 10.1109/TIP.2014.2323127
    [72] KHERADMAND A, MILANFAR P. A general framework for regularized, similarity-based image restoration[J]. IEEE Transactions on Image Processing, 2014, 23(12): 5136-5151. doi: 10.1109/TIP.2014.2362059
    [73] DANIELYAN A, KATKOVNIK V, EGIAZARIAN K O. BM3D frames and variational image deblurring[J]. IEEE Transactions on Image Processing, 2012, 21(4): 1715-1728. doi: 10.1109/TIP.2011.2176954
    [74] DONG W SH, SHI G M, LI X. Nonlocal image restoration with bilateral variance estimation: a low-rank approach[J]. IEEE Transactions on Image Processing, 2013, 22(2): 700-711. doi: 10.1109/TIP.2012.2221729
    [75] GU SH H, ZHANG L, ZUO W M, et al. . Weighted nuclear norm minimization with application to image denoising[C]. Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2014: 2862-2869.
    [76] YANG H, HU G SH, WANG Y Q, et al. Low-rank approach for image nonblind deconvolution with variance estimation[J]. Journal of Electronic Imaging, 2015, 24(6): 063013. doi: 10.1117/1.JEI.24.6.063013
    [77] TOMASI C, MANDUCHI R. Bilateral filtering for gray and color images[C]. Proceedings of the 6th International Conference on Computer Vision, IEEE, 1998: 839-846.
    [78] HE K M, SUN J, TANG X O. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397-1409. doi: 10.1109/TPAMI.2012.213
    [79] YANG H, ZHANG ZH B, ZHU M, et al. Edge-preserving image deconvolution with nonlocal domain transform[J]. Optics &Laser Technology, 2013, 54: 128-136.
    [80] SUN L B, CHO S, WANG J, et al. . Good image priors for non-blind deconvolution[C]. Proceedings of the 13th European Conference on Computer Vision, Springer, 2014: 231-246.
    [81] SCHMIDT U, ROTHER C, NOWOZIN S, et al. . Discriminative non-blind deblurring[C]. Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2013: 604-611.
    [82] ZORAN D, WEISS Y. From learning models of natural image patches to whole image restoration[C]. Proceedings of 2011 International Conference on Computer Vision, IEEE, 2011: 479-486.
    [83] ROTH S, BLACK M J. Fields of experts: a framework for learning image priors[C]. Proceedings of 2005 IEEE Computer Society IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2005: 860-867.
    [84] SCHMIDT U, ROTH S. Shrinkage fields for effective image restoration[C]. Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2014: 2774-2781.
    [85] CHEN Y J, YU W, POCK T. On learning optimized reaction diffusion processes for effective image restoration[C]. Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2015: 5261-5269.
    [86] REN W Q, ZHANG J W, MA L, et al. . Deep non-blind deconvolution via generalized low-rank approximation[C]. Proceedings of the 32nd International Conference on Neural Information Processing Systems, Curran Associates Inc. , 2018: 295-305.
    [87] KRUSE J, ROTHER C, SCHMIDT U. Learning to push the limits of efficient FFT-based image deconvolution[C]. Proceedings of 2017 IEEE International Conference on Computer Vision, IEEE, 2017: 4596-4604.
    [88] SON H, LEE S. Fast non-blind deconvolution via regularized residual networks with long/short skip-connections[C]. Proceedings of 2017 IEEE International Conference on Computational Photography, IEEE, 2017: 1-10.
    [89] SCHULER C J, BURGER H C, HARMELING S, et al. . A machine learning approach for non-blind image deconvolution[C]. Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2013: 1067-1074.
    [90] XU L, REN J S J, LIU C, et al. . Deep convolutional neural network for image deconvolution[C]. Proceedings of the 27th International Conference on Neural Information Processing Systems, MIT Press, 2014: 1790-1798.
    [91] EBOLI T, SUN J, PONCE J. End-to-end interpretable learning of non-blind image deblurring[C]. Proceedings of the 16th European Conference on Computer Vision, Springer, 2020: 314-331.
    [92] GONG D, ZHANG ZH, SHI Q F, et al. Learning deep gradient descent optimization for image deconvolution[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(12): 5468-5482. doi: 10.1109/TNNLS.2020.2968289
    [93] ZHANG K, ZUO W M, GU SH H, et al. . Learning deep CNN denoiser prior for image restoration[C]. Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2017: 2808-2817.
    [94] ZHANG J W, PAN J SH, LAI W SH, et al. . Learning fully convolutional networks for iterative non-blind deconvolution[C]. Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2017: 6969-6977.
    [95] DONG W SH, WANG P Y, YIN W T, et al. Denoising prior driven deep neural network for image restoration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(10): 2305-2318. doi: 10.1109/TPAMI.2018.2873610
    [96] RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[C]. Proceedings of 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2015: 234-241.
    [97] QUAN Y H, LIN P K, XU Y, et al. . Nonblind image deblurring via deep learning in complex field[J/OL]. IEEE Transactions on Neural Networks and Learning Systems, 2021: 1-14 (2021-04-14). https://ieeexplore.ieee.org/document/9404870/.
    [98] CHEN L, ZHANG J W, PAN J SH, et al. . Learning a non-blind deblurring network for night blurry images[C]. Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2021: 10537-10545.
    [99] NAN Y S, QUAN Y H, JI H. Variational-EM-based deep learning for noise-blind image deblurring[C]. Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2020: 3623-3632.
    [100] JIN M G, ROTH S, FAVARO P. Noise-blind image deblurring[C]. Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2017: 3834-3842.
    [101] DONG J X, ROTH S, SCHIELE B. DWDN: deep wiener deconvolution network for non-blind image deblurring[J/OL]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 (2021-12-28). https://ieeexplore.ieee.org/document/9664009/.
    [102] LEMPITSKY V, VEDALDI A, ULYANOV D. Deep image prior[C]. Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018: 9446-9454.
    [103] WANG ZH X, WANG Z P, LI Q Q, et al. . Image deconvolution with deep image and kernel priors[C]. Proceedings of 2019 IEEE/CVF International Conference on Computer Vision Workshop, IEEE, 2019: 980-989.
    [104] ZUKERMAN J, TIRER T, GIRYES R. BP-DIP: a backprojection based deep image prior[C]. Proceedings of the 28th European Signal Processing Conference, IEEE, 2021: 675-679.
    [105] BIGDELI S A, JIN M G, FAVARO P, et al. . Deep mean-shift priors for image restoration[C]. Proceedings of the 31st International Conference on Neural Information Processing Systems, Curran Associates Inc. , 2017: 763-772.
    [106] LEVIN A, WEISS Y, DURAND F. Understanding and evaluating blind deconvolution algorithms[C]. Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2009: 1964-1971.
    [107] SUN L B, CHO S, WANG J, et al. . Edge-based blur kernel estimation using patch priors[C]. Proceedings of IEEE International Conference on Computational Photography, IEEE, 2013: 1-8.
    [108] MARTIN D, FOWLKES C, TAL D, et al. . A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics[C]. Proceedings of the Eighth IEEE International Conference on Computer Vision, IEEE, 2001: 416-423.
    [109] DONG J X, PAN J SH, SUN D Q, et al. . Learning data terms for non-blind deblurring[C]. Proceedings of the 15th European Conference on Computer Vision, Springer, 2018: 777–792.
    [110] DONG J X, ROTH S, SCHIELE B. Learning spatially-variant MAP models for non-blind image deblurring[C]. Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2021: 4884-4893.
    [111] REN D W, ZUO W M, ZHANG D, et al. Simultaneous fidelity and regularization learning for image restoration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(1): 284-299. doi: 10.1109/TPAMI.2019.2926357
    [112] WANG ZH, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612. doi: 10.1109/TIP.2003.819861
    [113] REN D W, ZUO W M, ZHANG D, et al. Partial deconvolution with inaccurate blur kernel[J]. IEEE Transactions on Image Processing, 2018, 27(1): 511-524. doi: 10.1109/TIP.2017.2764261
    [114] JI H, WANG K. Robust image deblurring with an inaccurate blur kernel[J]. IEEE Transactions on Image Processing, 2012, 21(4): 1624-1634. doi: 10.1109/TIP.2011.2171699
    [115] VASU S, MALIGIREDDY V R, RAJAGOPALAN A N. Non-blind deblurring: handling kernel uncertainty with CNNs[C]. Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018: 3272-3281.
    [116] NAN Y S, JI H. Deep learning for handling kernel/model uncertainty in image deconvolution[C]. Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2020: 2385-2394.
    [117] WHYTE O, SIVIC J, ZISSERMAN A, et al. Non-uniform deblurring for shaken images[J]. International Journal of Computer Vision, 2012, 98(2): 168-186. doi: 10.1007/s11263-011-0502-7
    [118] TAI Y W, TAN P, BROWN M S. Richardson-Lucy deblurring for scenes under a projective motion path[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1603-1618. doi: 10.1109/TPAMI.2010.222
    [119] HIRSCH M, SCHULER C J, HARMELING S, et al. . Fast removal of non-uniform camera shake[C]. Proceedings of 2011 International Conference on Computer Vision, IEEE, 2011: 463-470.
    [120] SUN J, CAO W F, XU Z B, et al. . Learning a convolutional neural network for non-uniform motion blur removal[C]. Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2015: 769-777.
    [121] KUPYN O, BUDZAN V, MYKHAILYCH M, et al. . DeblurGAN: blind motion deblurring using conditional adversarial networks[C]. Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018: 8183-8192.
    [122] PAN J SH, SUN D Q, PFISTER H, et al. . Blind image deblurring using dark channel prior[C]. Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2016: 1628-1636.
    [123] CHEN L, FANG F M, WANG T T, et al. . Blind image deblurring with local maximum gradient prior[C]. Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2019: 1742-1750.
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  • 收稿日期:  2022-05-16
  • 修回日期:  2022-06-20
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