Adaptive bi-lp-l2-norm based blind super-resolution reconstruction for single blurred image

doi:10.11772/j.issn.1001-9081.2017.08.2313

Abstract

Abstract: An adaptive bi-l_p-l₂-norm based blind super-resolution reconstruction method was proposed to improve the quality of a low-resolution blurred image, which includes independent blur-kernel estimation sub-process and non-blind super-resolution reconstruction sub-process. In the blur-kernel estimation sub-process, the bi-l_p-l₂-norm regularization was imposed on both the sharp image and the blur-kernel. Moreover, by introducing threshold segmentation of image gradients, the l_p-norm and the l₂-norm constraints on the sharp image were adaptively combined. With the estimated blur-kernel, the non-blind super-resolution reconstruction method based on non-locally centralized sparse representation was used to reconstruct the final high-resolution image. In the simulation experiments, compared with the bi-l₀-l₂-norm based method, the average Peak Signal-to-Noise Ratio (PSNR) gain of the proposed method was 0.16 dB higher, the average Structural Similarity Index Measure (SSIM) gain was 0.0045 higher, and the average reduction of Sum of Squared Difference (SSD) ratio was 0.13 lower. The experimental results demonstrate a superior performance of the proposed method in terms of kernel estimation accuracy and reconstructed image quality.

Key words: super-resolution, blind reconstruction, blur-kernel estimation, penalty function, augmented Lagrangian method

摘要： 为了提高低分辨率模糊图像的质量，提出了一种基于自适应双l_p-l₂范数的超分辨率盲重建方法。该方法分为模糊核估计子过程和超分辨率非盲重建子过程。在模糊核估计子过程中，使用双l_p-l₂范数先验同时约束锐化图像和模糊核的估计，并使用图像梯度的阈值分割，实现锐化图像l_p-l₂范数约束的自适应组合；在超分辨率非盲重建子过程中，结合估计到的模糊核，使用基于非局部中心化稀疏表示的超分辨率方法重建出最终的高分辨率图像。仿真实验中，与基于双l₀-l₂范数的方法相比，该算法重建结果的平均峰值信噪比（PSNR）提高了0.16 dB，平均结构相似度（SSIM）提高了0.0045，平均差方和比降低了0.13。实验结果表明，所提方法能估计出较准确的模糊核，最终的重建图像中，振铃得到有效抑制，图像质量较好。

关键词: 超分辨率, 盲重建, 模糊核估计, 罚函数, 增广拉格朗日法

CLC Number:

TP391.41

LI Tao, HE Xiaohai, TENG Qizhi, WU Xiaoqiang. Adaptive bi-l_p-l₂-norm based blind super-resolution reconstruction for single blurred image[J]. Journal of Computer Applications, 2017, 37(8): 2313-2318.

李滔, 何小海, 滕奇志, 吴小强. 基于自适应双l_p-l₂范数的单幅模糊图像超分辨率盲重建[J]. 计算机应用, 2017, 37(8): 2313-2318.

References

[1] ZHANG X, WU X. Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation[J]. IEEE Transactions on Image Processing, 2008, 17(6):887-896.
[2] 季成涛,何小海,符耀庆,等.一种基于正则化的边缘定向插值算法[J]. 电子与信息学报,2014,36(2):293-297. (JI C T, HE X H, FU Y Q, et al. An edge directed interpolation algorithm based on regularization[J]. Journal of Electronics & Information Technology, 2014, 36(2):293-297.)
[3] YAN Q, XU Y, YANG X, et al. Single image superresolution based on gradient profile sharpness[J]. IEEE Transactions on Image Processing, 2015, 24(10):3187-3202.
[4] REN C, HE X, TENG Q, et al. Single image super-resolution using local geometric duality and non-local similarity[J]. IEEE Transactions on Image Processing, 2016, 25(5):2168-2183.
[5] ZEYDE R, ELAD M, PROTTER M. On single image scale-up using sparse-representations[C]//Proceedings of the 2010 International Conference on Curves and Surfaces, LNCS 6920. Berlin:Springer-Verlag, 2010:711-730.
[6] TIMOFTE R, DE SMET V, VAN GOOL L. A+:Adjusted anchored neighborhood regression for fast super-resolution[C]//ACCV 2014:Proceedings of the 2014 Asian Conference on Computer Vision, LNCS 9006. Berlin:Springer-Verlag, 2014:111-126.
[7] LI T, HE X, TENG Q, et al. Rotation expanded dictionary-based single image super-resolution[J]. Neurocomputing, 2016, 216:1-17.
[8] EFRAT N, GLASNER D, APARTSIN A, et al. Accurate blur models vs. image priors in single image super-resolution[C]//ICCV 2013:Proceedings of the 2013 IEEE International Conference on Computer Vision. Washington, DC:IEEE Computer Society, 2013:2832-2839.
[9] LEVIN A, WEISS Y, DURAND F, et al. Understanding and evaluating blind deconvolution algorithms[C]//CVPR 2009:Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2009:1964-1971.
[10] BÉGIN I, FERRIE F P. PSF recovery from examples for blind super-resolution[C]//ICIP 2007:Proceedings of the 2007 IEEE International Conference on Image Processing. Piscataway, NJ:IEEE, 2007:421-424.
[11] WANG Q, TANG X, SHUM H. Patch based blind image super resolution[C]//ICCV 2005:Proceedings of the 2005 IEEE International Conference on Computer Vision. Piscataway,NJ:IEEE, 2005:709-716.
[12] HE Y, YAP K-H, CHEN L, et al. A soft MAP framework for blind super-resolution image reconstruction[J]. Image and Vision Computing, 2009, 27(4):364-373.
[13] MICHAELI T, IRANI M. Nonparametric blind super-resolution[C]//ICCV 2013:Proceedings of the 2013 IEEE International Conference on Computer Vision. Washington, DC:IEEE Computer Society, 2013:945-952.
[14] SHAO W-Z, ELAD M. Simple, accurate, and robust nonparametric blind super-resolution[C]//ICIG 2015:Proceedings of the 8th International Conference on Image and Graphics, LNCS 921. Berlin:Springer, 2015:333-348.
[15] CHARTRAND R, YIN W. Iteratively reweighted algorithms for compressive sensing[C]//ICASSP 2008:Proceedings of the 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway, NJ:IEEE, 2008:3869-3872.
[16] MAZUMDER R, FRIEDMAN J H, HASTIE T. SparseNet:Coordinate descent with nonconvex penalties[J]. Journal of the American Statistical Association, 2011, 106(495):1125-1138.
[17] DONG W, ZHANG L, SHI G, et al. Nonlocally centralized sparse representation for image restoration[J]. IEEE Transactions on Image Processing, 2013, 22(4):1620-1630.
[18] KRISHNAN D, TAY T, FERGUS R. Blind deconvolution using a normalized sparsity measure[C]//CVPR 2011:Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2011:233-240.
[19] KOTERA J, ŠROUBEK F, MILANFAR P. Blind deconvolution using alternating maximum a posteriori estimation with heavy-tailed priors[C]//CAIP 2013:Proceedings of the 2013 International Conference on Computer Analysis of Images and Patterns, LNCS 8048. Berlin:Springer-Verlag, 2013:59-66.
[20] XU L, ZHENG S, JIA J. Unnatural L₀ sparse representation for natural image deblurring[C]//CVPR 2013:Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2013:1107-1114.
[21] 闫敬文,彭鸿,刘蕾,等.基于L_0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. (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.)
[22] SHAO W-Z, LI H-B, ELAD M. Bi-l₀-l₂-norm regularization for blind motion deblurring[J]. Journal of Visual Communication and Image Representation, 2015, 33:42-59.
[23] CANDÈS E J, WAKIN M B, BOYD S P. Enhancing sparsity by reweighted l₁ minimization[J]. Journal of Fourier Analysis and Applications, 2008, 14(5/6):877-905.
[24] SHE Y. An iterative algorithm for fitting nonconvex penalized generalized linear models with grouped predictors[J]. Computational Statistics & Data Analysis, 2012, 56(10):2976-2990.
[25] KRISHNAN D, FERGUS R. Fast image deconvolution using hyper-Laplacian priors[C]//NIPS 2009:Proceedings of the 200923rd Advances in Neural Information Processing Systems. Cambridge, MA:MIT Press, 2009:1033-1041.

Adaptive bi-l_p-l₂-norm based blind super-resolution reconstruction for single blurred image

基于自适应双l_p-l₂范数的单幅模糊图像超分辨率盲重建

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	NIU Kangli, CHEN Yuzhang, SHEN Junfeng, ZENG Zhangfan, PAN Yongcai, WANG Yichong. Dual-channel night vision image restoration method based on deep learning [J]. Journal of Computer Applications, 2021, 41(6): 1775-1784.
[2]	LIANG Min, WANG Haorong, ZHANG Yao, LI Jie. Image super-resolution reconstruction method based on accelerated residual network [J]. Journal of Computer Applications, 2021, 41(5): 1438-1444.
[3]	BIAN Pengcheng, ZHENG Zhonglong, LI Minglu, HE Yiran, WANG Tianxiang, ZHANG Dawei, CHEN Liyuan. Attention fusion network based video super-resolution reconstruction [J]. Journal of Computer Applications, 2021, 41(4): 1012-1019.
[4]	DUAN Youxiang, ZHANG Hanxiao, SUN Qifeng, SUN Youkai. Image super-resolution reconstruction algorithm based on Laplacian pyramid generative adversarial network [J]. Journal of Computer Applications, 2021, 41(4): 1020-1026.
[5]	WANG Yongjin, ZUO Yu, WU Lian, CUI Zhongwei, ZHAO Chenjie. Image super-resolution reconstruction based on attention mechanism [J]. Journal of Computer Applications, 2021, 41(3): 845-850.
[6]	Meng LI, Pinle QIN, Jianchao ZENG, Junbo LI. Super-resolution and multi-view fusion based on magnetic resonance image inter-layer interpolation [J]. Journal of Computer Applications, 2021, 41(11): 3362-3367.
[7]	LIN Jing, HUANG Yuqing, LI Leimin. Image super-resolution reconstruction based on spherical moment matching and feature discrimination [J]. Journal of Computer Applications, 2020, 40(8): 2345-2350.
[8]	SHEN Marui, LI Jincheng, ZHANG Ya, ZOU Jian. Magnetic resonance image reconstruction algorithm via non-convex total variation regularization [J]. Journal of Computer Applications, 2020, 40(8): 2358-2364.
[9]	HU Gaopeng, CHEN Ziliu, WANG Xiaoming, ZHANG Kaifang. Image super-resolution reconstruction based on deep progressive back-projection attention network [J]. Journal of Computer Applications, 2020, 40(7): 2077-2083.
[10]	HU Xueying, GUO Hairu, ZHU Rong. Image super-resolution reconstruction based on hybrid deep convolutional network [J]. Journal of Computer Applications, 2020, 40(7): 2069-2076.
[11]	DAI Qiang, CHENG Xi, WANG Yongmei, NIU Ziwei, LIU Fei. Light-weight automatic residual scaling network for image super-resolution reconstruction [J]. Journal of Computer Applications, 2020, 40(5): 1446-1452.
[12]	CHEN Saijian, ZHU Yuanping. Joint super-resolution and deblurring method based on generative adversarial network for text images [J]. Journal of Computer Applications, 2020, 40(3): 859-864.
[13]	FAN Fan, GAO Yuan, QIN Pinle, WANG Lifang. Abdominal MRI image multi-scale super-resolution reconstruction based on parallel channel-spatial attention mechanism [J]. Journal of Computer Applications, 2020, 40(12): 3624-3630.
[14]	YE Yang, CAI Qiong, DU Xiaobiao. Single image super-resolution method based on non-local channel attention mechanism [J]. Journal of Computer Applications, 2020, 40(12): 3618-3623.
[15]	SUN Zhongfan, ZHOU Zhenghua, ZHAO Jianwei. Super-resolution reconstruction method with arbitrary magnification based on spatial meta-learning [J]. Journal of Computer Applications, 2020, 40(12): 3471-3477.