基于结构字典学习的图像复原方法

杨航; 吴笑天; 王宇庆

doi:10.3788/CO.20171002.0207

Volume 10 Issue 2

Apr. 2017

Turn off MathJax

Article Contents

Chinese Optics > 2017 > 10(2): 207-218.

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

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

PDF( 9600 KB)

Image restoration approach based on structure dictionary learning

doi: 10.3788/CO.20171002.0207

Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China

Funds:

National Natural Science Foundation of China 61401425

Received Date: 12 Oct 2016
Rev Recd Date: 05 Dec 2016
Publish Date: 01 Apr 2017

Abstract

Abstract

In this paper, we propose a new structure dictionary learning method, and perform image restoration based on this approach. First, we define the structure dictionary for the nature image. Second, an iterative algorithm is proposed with the decouple of deblurring and denoising steps in the restoration process, which effectively integrates the Fourier regularization and structure dictionary learning technique into the deconvolution framework. Specifically, we propose an iterative algorithm. In the deblurring step, we involve a regularized inversion of the blur in Fourier domain. Then we remove the remained noise using the structure dictionary learning method in the denoising step. Experiment results show that this approach outperforms 6 state-of-the-art image deconvolution methods in terms of improvement signal to noise rate (ISNR) and visual quality, and the ISNR can be improved by more than 0.5 dB.
- structure dictionary,
- dictionary learning,
- image restoration,
- deconvolution

FullText(HTML)

References(38)

References

[1]	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. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB200909012.htm SHEN H, LI S M, MAO J G, et al.. Digital image restoration techniques:a review[J]. J. Image and Graphics, 2009, 14(9):1764-1775.(in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB200909012.htm
[2]	杨亚威, 胡双演, 张士杰, 等.基于字典对联合学习的退化图像复原方法[J].计算机辅助设计与图形学学报, 2015, 3:406-413. http://www.cnki.com.cn/Article/CJFDTOTAL-JSJF201503005.htm YANG Y W, HU SH Y, ZHANG SH J, et al.. A degraded image restoration approach based on pairs of dictionaries jointly learning[J]. J. Computer-Aided Design & Computer Graics, 2015, 3:406-413.(in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-JSJF201503005.htm
[3]	陈曦, 汪彦刚, 彭思龙, 等.部分模糊核已知的混合模糊图像复原算法[J].计算机辅助设计与图形学学报, 2010, 2:272-278. http://www.cnki.com.cn/Article/CJFDTOTAL-JSJF201002015.htm CHEN X, WANG Y G, PENG S L, et al.. Restoration of degraded image from partially known mixed blur[J]. J. Computer-Aided Design & Computer Graphics, 2010, 2:272-2781.(in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-JSJF201002015.htm
[4]	JAIN A K. Fundamental of Digital Image Processing[M]. Prentice-Hall. Inc., 1989:1420-1424.
[5]	朱明, 杨航, 贺柏根, 等.联合梯度预测与导引滤波的图像运动模糊复原[J].中国光学, 2013, 6(6):850-855. http://www.chineseoptics.net.cn/CN/abstract/abstract9084.shtml ZHU M, YANG H, HE B G, et al.. Image motion blurring restoration of joint gradient prediction and guided filter[J]. Chinese Optics, 2013, 6(6):850-855.(in Chinese) http://www.chineseoptics.net.cn/CN/abstract/abstract9084.shtml
[6]	刘成云, 常发亮.基于稀疏表示和Weber定律的运动图像盲复原[J].光学精密工程, 2015, 23(2):600-608. doi: 10.3788/OPE. LIU C Y, CHANG F L. Blind moving image restoration based on sparse representation and Weber's law[J]. Opt. Precision Eng., 2015, 23(2):600-608.(in Chinese) doi: 10.3788/OPE.
[7]	BANHAM M R, KATSAGGELOS A K. Digital image restoration[J]. IEEE Siggcal Processing Magazine, 1997, 14(2):24-41. doi: 10.1109/79.581363
[8]	HANSEN P C. Rank-deficient and Discrete ill-posed Problems:Numerical Aspects of Linear Inversion[M]. Philadelphia, PA:SIAM, 1998.
[9]	WANG Y, YANG J, YIN W, et al.. Anew alternating minimization algorithm for total variation image reconstruction[J]. SIAM J. Imag. Sci., 2008, 1(3):248-272. doi: 10.1137/080724265
[10]	OLIVEIRA J, BIOUCAS-DIAS J M, FIGUEIREDO M A, et al.. 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
[11]	DAUBECHIES I, DEFRISE M, AND MOL C, et al.. An iterativethresholding algorithm for linear inverse problemswith a sparsity constraint[J]. Communications on Pure and Applied Mathematics, 2004, 57(11):1413-1457. doi: 10.1002/(ISSN)1097-0312
[12]	BECK A, TEBOULLE M A. Fast iterativeshrinkage-thresholding algorithm for linear inverseproblems[J]. SIAM J. Imag. Sci., 2009(2):183-202. https://www.researchgate.net/publication/220124383_A_Fast_Iterative_Shrinkage-Thresholding_Algorithm_for_Linear_Inverse_Problems
[13]	BIOUCAS-DIAS J, FIGUEIREDO M. A new TwIST:two-step iterative shrinkage/thresholding algorithms for image restoration[J]. IEEE Trans. Image Process, 2007, 16(12):2992-3004. doi: 10.1109/TIP.2007.909319
[14]	张振东, 陈健, 王伟国, 等.基于SSIM_NCCDFT的超分辨率复原评价方法研究[J].液晶与显示, 2015, 30(4):713-721. doi: 10.3788/YJYXS ZHANG ZH D, CHEN J, WANG W G, et al.. Evaluation method of super-resolution restoration based on SSIM_NCCDFT[J]. Chinese J. Liquid Crystals and Displays, 2015, 30(4):713-721.(in Chinese) doi: 10.3788/YJYXS
[15]	郭萌, 赵岩, 王世刚, 等.基于区域选择的红外弱小目标超分辨率复原算法[J].液晶与显示, 2016, 31(4):415-420. doi: 10.3788/YJYXS GUO M, ZHAO Y, WANG SH G, et al.. Infrared dim-small target super-resolution restoration algorithm based on region selection[J]. Chinese J. Liquid Crystals and Displays, 2016, 31(4):415-420.(in Chinese) doi: 10.3788/YJYXS
[16]	NEELAMANI R, CHOI H, BARANIUK R G. ForWaRD:Fourier-wavelet regularized deconvolution for ill-conditioned systems[J]. IEEE Trans. Signal Process, 2004, 52(2):418-433.(in Chinese) doi: 10.1109/TSP.2003.821103
[17]	PATEL V M, EASLEY G R, HEALY D M. Shearlet-based deconvolution[J]. IEEE Trans. Image Process, 2009, 18(12):2673-2685. doi: 10.1109/TIP.2009.2029594
[18]	YANG H, ZHANG Z 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):67-75. https://www.researchgate.net/publication/258687989_Fusion_of_wave_atom-based_Wiener_shrinkage_filter_and_joint_non-local_means_filter_for_texture-preserving_image_deconvolution
[19]	GUERRERO-COLON J A, MANCERA L, PORTILL J. Image restoration using space-variant Gaussian scale mixtures in overcomplete pyramids[J]. IEEE Trans. Image Process, 2007, 17(1):27-41. https://www.researchgate.net/publication/5621703_Image_Restoration_Using_Space-Variant_Gaussian_Scale_Mixtures_in_Overcomplete_Pyramids
[20]	FOI A, DABOV K, KATKOVNIK V, et al.. Shape-adaptive DCT for denoising and image reconstruction[J]. SPIE, 2006, 6064:203-214. https://www.researchgate.net/publication/240237642_Shape-adaptive_DCT_for_denoising_and_image_reconstruction_-_art_no_60640N
[21]	DABOV K, FOI A, KATKOVNIK V, et al.. Image denoising by sparse 3D transform-domain collaborative filtering[J]. IEEE Trans. Image Process, 2007, 16(8):2080-2095. doi: 10.1109/TIP.2007.901238
[22]	DABOVE K, FOI A, Image restoration by sparse 3D transform-domain collaborative filtering[J]. SPIE, 2008, 6812:681207-12.
[23]	YANG H, ZHU M, ZHANG Z B, et al.. Guided filter based edge-preserving image non-blind deconvolution[C]. Proc. 20th IEEE International Conference on Image Processing (ICIP), Melbourne, Australia, 2013:4593-4596.
[24]	YANG H, ZHANG Z B, ZHU M, et al.. Edge-preserving image deconvolution with nonlocal domain transform″[J]. Optics and Laser Technology, 2013, 54:128-136. doi: 10.1016/j.optlastec.2013.05.020
[25]	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
[26]	COUZINIE-DEVY F, MAIRAL J, BACH F, et al.. Dictionary learning for deblurring and digital zoom[EB/OL].[2011-09-28].https://hal.inria.fr/inria-00627402.
[27]	JAIN A K, CAO K. Fingerprint image analysis:role of orientation patch and ridge structure dictionaries[J]. Geometry Driven Statistics, 2015:121-288.
[28]	LI K, SHAO F, JIANG G, et al.. Joint structure texture sparse coding for quality prediction of stereoscopic images[J]. Electronics Letters, 2015, 51(24):1994-1995. doi: 10.1049/el.2015.2049
[29]	YUAN L, SUN J, QUAN L, et al.. Progressive inter-scale and intra-scale non-blind image deconvolution[J]. ACM Transaction on Graphics, 2008, 27(3):74-83. https://www.researchgate.net/publication/220183655_Progressive_inter-scale_and_intra-scale_non-blind_image_deconvolution
[30]	PORTILLA J. Image restoration through l0 analysis based sparse optimization in tight frames[C]. Proc. 16th IEEE ICIP, Cairo, Egypt, 2009:3909-3912.
[31]	NI J, TURAGA P, PATEL V M, CHELLAPPA R. Example-driven manifold priors for image deconvolution[J]. IEEE Trans. Image Process, 2011, 20(11):3086-3096. doi: 10.1109/TIP.2011.2145386
[32]	XUE F, LUISIER F, BLU T. Multi-wiener SURE-LET deconvolution[J]. IEEE Trans. Image Process, 2013, 22(5):1954-1968. doi: 10.1109/TIP.2013.2240004
[33]	CHATTERJEE P, MILANFAR P. Clustering-based denoising with locally learned dictionaries[J]. IEEE Trans. Image Process, 2009, 18(7):1438-1451. doi: 10.1109/TIP.2009.2018575
[34]	MAIRAL J, BACH F, PONCE J, et al.. Non-local sparse models for image restoration[C]. Proc. IEEE Int. Conf. Comput. Vis., Kyoto, Japan, 2009:2272-2279.
[35]	LIU Q G, WANG S S, YING L L, et al.. Adaptive dictionary learning in sparse gradient domain for image recovery[J]. IEEE Transactions on Image Processing, 2013, 22(12):4652-4663. doi: 10.1109/TIP.2013.2277798
[36]	STARCK J L, ELAD M, DONOHO D, et al.. Redundant multiscale transforms and their application for morphological component analysis[J]. J. Advances in Imaging and Electron Physics, 2004, 132:287-348. doi: 10.1016/S1076-5670(04)32006-9
[37]	ZHANG Q, SHEN X Y, XU L, et al.. Rolling guidance filter[C]. Europ. Conf.Comput.Vision. Springer, 2014:815-830.
[38]	YANG H, ZHU M, WU X T, et al.. Dictionary learning approach for image deconvolution with variance estimation[J]. Applied Optics, 201453(24):5677-5683. https://www.researchgate.net/publication/265057447_Dictionary_learning_approach_for_image_deconvolution_with_variance_estimation