Computer Science ›› 2017, Vol. 44 ›› Issue (Z11): 169-174.doi: 10.11896/j.issn.1002-137X.2017.11A.035

Previous Articles     Next Articles

Self-learning Single Image Super-resolution Reconstruction Based on Compressive Sensing and SVR

QIN Xu-jia, SHAN Yang-yang, XIAO Jia-ji, ZHENG Hong-bo and ZHANG Mei-yu   

  • Online:2018-12-01 Published:2018-12-01

Abstract: For the long learning time and the easiness to occur wrong and high frequecy details of super-resolution(SR) reconstruction algorithm which traditionally depends on external image database,this paper presented a single image SR reconstruction method based on compressive sensing(CS) and support vector regression(SVR). SVR model is training for degarded image itself to make full use of the self similarity of the image.In training stage,we firstly detected image edge and classified image patch into low and high frequency blocks.Then we did image block sparse coding,and trained a SVR model using image’s label vector and sparse representation matrix.Finally,we predicted the high resolution(HR) image with SVR using this model in the testing stage.Experiments show that the proposed method is more rea-listic than the method based on external library,and the edge is more clear than bicubic interpolation method.

Key words: Super-resolution reconstruction,Compressive sensing,Support vector regression(SVR),Bicubic interpolation

[1] LIN Z C,SHUM H Y.Fundamental limits of reconstruction-based super-resolution algorithms under local translation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(1):83-97.
[2] YANG J C,WRIGHT J,HUANG T.Image super-resolution as sparse representation of raw image patches[C]∥Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition.Alaska:IEEE Computer Press,2008:1-8.
[3] FREEMAN W T,PASZTOR E C,CARMICHAEL O T.Learning low-level vision[J].International Journal of Computer Vision,2000,40(1):25-47.
[4] DAI S,HAN M,XU W,et al.Soft edge smoothness prior for alpha channel super resolution[C]∥Proceedings of IEEE International Conference on Computer Vision.Washington,DC:IEEE Computer Press,2007:5-8.
[5] DURARLE M F,DAVENPORT M A.Single-Pixel Imaging via Compressive Sampling[J].IEEE Signal Processing Magazine,2008,25(2):83-91.
[6] YANG M C,WANG Y C F.A Self-Learning Approach to Single Image Super-Resolution[J].IEEE Transactions on Multimedia,2013,15(3):498-508.
[7] ZHANG K B,GAO X B,TAO D C.Multi-scale dictionary for single image super-resolution[C]∥IEEE Conference on Computer Vision and Pattern Recognition.Alaska,IEEE Computer Press,2012:1114-1121.
[8] YU G S,GUILLERMO S,STPHANE M.Solving Inverse Pro-blems With Piecewise Linear Estimators:From Gaussian Mixture Models to Structured Sparsity[J].IEEE Transactions on Image Processing,2012,21(5):2418-2499.
[9] PELEG T,ELAD M.A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution[J].IEEE Transacations on Image Processing,2014,23(6):2569-2582.
[10] LI D L,MERSEREAU R M,SIMSKE S J.Blind Image Deconvolution Using Support Vector Regression[C]∥Proceedings IEEE International Conference on Acoustics,Speech and Signal Process.Washington,DC:IEEE Computer Press,2005:113-116.
[11] LI D L,RUSSELL S S.Single Image super-resolution based on support vector regression[C]∥Proceedings of International Joint Conference on Neural Networks.Washington,DC:IEEE Computer Press,2006:2898-2901.
[12] YANG M C,CHU C T,WANG YC F.Learning Sparse Image Representation With Support Vector Regression For Single Image Super Resolution[C]∥IEEE International Conference on Image Processing.Washington,DC:IEEE Computer Press,2010:1973-1976.
[13] GLASNER D,BAGON S,IRANI M.Super-Resolution From a Single Image[C]∥Proceedings of 2009 IEEE 12th International Conference on Computer Vision.Kyoto:IEEE Computer Press,2009:349-356.
[14] ZONTAK M,IRANI M.Internal Statistics of a Single Natural Image[C]∥Proceedings of 2011 IEEE Conference on Computer Vision and Pattern Recognition.America:Colorado Springs,2011:977-984.
[15] MAIRAL J,BACH F,PONCE J.Online Learning for Matrix Factorization and Sparse Coding[J].Journal of Machine Lear-ning Theory,2010,11(1):19-60.
[16] TAPPEN M F,RUSSELL B C,FREEMAN W T.Exploiting the Sparse Derivative Prior for Super-Resolution and Image Demosaicing[C]∥proceedings of IEEE workshop on Statistical and Computational Theories of Vision.Fort Collins:IEEE Computer Press,2003:1-24.
[17] EFRON B,HASTIE T,JOHNSTONE I.Least Angle Regression[J].The Annals of Statistics,2004,32(2):407-499.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!