Computer Science ›› 2018, Vol. 45 ›› Issue (3): 263-267.doi: 10.11896/j.issn.1002-137X.2018.03.042

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Single Video Super-resolution Algorithm Based on Non-local Means and Total Variation Minimization

CHEN Cheng, CHANG Kan, MO Cai-wang, LI Tian-yi and QIN Tuan-fa   

  • Online:2018-03-15 Published:2018-11-13

Abstract: The traditional reconstruction-based single video super-resolution algorithms are able to solve the video super-resolution problem well.However,the existing algorithms have not fully exploited the correlation in intra-frames and inter-frames,which leaves much space for further improvement.This paper proposed a new single video super-resolution algorithm to solve this problem.When exploiting the spatial correlations,the non-local means model is used to get the non-local structural property and the total variation model is utilized to get the local structural property.In order to exploit inter-frame correlation,optical flow method is applied to perform inter-frame estimation.Finally,to solve the established optimization problem,a split-Bregman method based fast iteration algorithm was proposed.The experimental results demonstrate the effectiveness of the proposed algorithm.Compared with other algorithms,the proposed algorithm is able to achieve better subjective and objective results.

Key words: Video super-resolution,Non-local means,Total variation,Optical flow

[1] XU Y M,SONG J W,XIAO X J.Super resolution algorithm based on sub-pixel block matching and dictionary learning[J].Computer Science,2016,3(8):304-308.(in Chinese) 徐煜明,宋佳伟,肖贤建.基于亚像素块匹配和字典学习的超分辨率算法[J].计算机科学,2016,3(8):304-308.
[2] HUNG E M,GARCIA D C,DE QUEIROZ R L.Example-based enhancement of degraded video[J].IEEE Signal Processing Letters,2014,21(9):1140-1144.
[3] HUNG E M,DE QUEIROZ R L,BRANDI F,et al.Video super-resolution using codebooks derived From key-Frames[J].IEEE Transactions on Circuits and Systems for Video Technology,2012,9(22):1321-1331.
[4] LI L H,DU J P,LIANG M Y,et al.Video super-resolution algorithm based on spatial-temporal feature and neural network[J].Journal of Beijing University of Posts and Telecommunications,2016,39(4):1-6.(in Chinese) 李玲慧,杜军平,梁美玉,等.基于时空特征和神经网络的视频超分辨率算法[J].北京邮电大学学报,2016,39(4):1-6.
[5] LEDIG C,THEIS L,HUSZR F,et al.Photo-realistic singleimage super-resolution using a generative adversarial network[J].arXiv preprint arXiv:1609.04802,2016.
[6] RUDIN L I,OSHER S,FATEMI E.Nonlinear total variation based noise removal algorithms[J].Physica D:Nonlinear Phenomena,1992,60(1):259-268.
[7] LI C,YIN W,JIANG H,et al.An efficient augmented Lagran-gian method with applications to total variation minimization[J].Computational Optimization and Applications,2013,56(3):507-530.
[8] FARSIU S,ROBINSON M D,ELAD M,et al.Fast and robust multiframe super resolution[J].IEEE Transactions on Image Processing,2004,13(10):1327-1344.
[9] YUAN Q,ZHANG L,SHEN H.Regional spatially adaptive total variation super-resolution with spatial information filtering and clustering[J].IEEE Transactions on Image Processing,2013,22(6):2327-2342.
[10] BELEKOS S P,GALATSANOS N P,KATSAGGELOS A K.Maximum a posteriori video super-resolution using a new multichannel image prior[J].IEEE Transactions on Image Proces-sing,2010,19(6):1451-1464.
[11] ZHANG Y L,GAN Z L,ZHU X C.Video super-resolutionmethod based on similarity constraints[J].Journal of Image and Graphics,2013,18(7):761-767.(in Chinese) 张义轮,干宗良,朱秀昌.相似性约束的视频超分辨率重建[J].中国图象图形学报,2013,18(7):761-767.
[12] ZHU Y,LI K,JIANG J.Video super-resolution based on automatic key-frame selection and feature-guided variational optical flow[J].Signal Processing:Image Communication,2014,29(8):875-886.
[13] LI K,ZHU Y,YANG J,et al.Video super-resolution using anadaptive superpixel-guided auto-regressive model[J].Pattern Recognition,2016,51(C):59-71.
[14] LU S,OSHER S J.Decomposition of images by the ani-sotropic Rudin-Osher-Fatemimodel[J].Communications on Pure and Applied Mathematics,2004,57(12):1609-1626.
[15] BUADES A,COLL B,MOREL J M.A non-local algorithm for image denoising[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005(CVPR 2005).IEEE,2005:60-65.
[16] SONG B C,JEONG S C,CHOI Y.Video super-resolution algorithm using bi-directional overlapped block motion compensation and on-the-fly dictionary training[J].IEEE Transactions on Circuits and Systems for Video Technology,2011,21(3):274-285.
[17] KIM K I,KWON Y.Single-image super-resolution using sparse regression and natural image prior[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(6):1127-1133.
[18] BRANDI F,QUEIROZ R,MUKHERJEE D.Super-resolution of video using key frames and motion estimation[C]∥2008 15th IEEE International Conference on Image Processing.IEEE,2008:321-324.
[19] GOLDSTEIN T,OSHER S.The split Bregman method for L1-regularized problems[J].SIAM Journal on Imaging Sciences,2009,2(2):323-343.

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