Volume 43 Issue 1
Sep 2022
Turn off MathJax
Article Contents
JOURNAL OF MECHANICAL ENGINEERING  > 2017  >  43(1): 43-48.
DU Weinan, HU Yongli, SUN Yanfeng. Image Super-resolution Reconstruction Based on Residual Dictionary Learning[J]. JOURNAL OF MECHANICAL ENGINEERING, 2017, 43(1): 43-48. doi: 10.11936/bjutxb2016060049
Citation: DU Weinan, HU Yongli, SUN Yanfeng. Image Super-resolution Reconstruction Based on Residual Dictionary Learning[J]. JOURNAL OF MECHANICAL ENGINEERING, 2017, 43(1): 43-48. doi: 10.11936/bjutxb2016060049
shu

Image Super-resolution Reconstruction Based on Residual Dictionary Learning

doi: 10.11936/bjutxb2016060049

  • Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing University of Technology, Beijing 100124, China

  • Received Date: 16 Jun 2016
    Available Online: 09 Sep 2022
  • Issue Publish Date: 01 Jan 2017
    Abstract
  • In this paper, an image super-resolution method was proposed based on 2D separable dictionary and residual dictionary to improve the quality of reconstructed image and to preserve space information as well as high-frequency information. Unlike the conventional super-resolution method based on 1D dictionary, the 2D separable dictionary was constructed from a set of training images in 2D matrix form without vectorization, so it had the capacity of learning the inherent texture structure of images. Additionally, it was a pair of compact dictionaries that were of smaller size and less storage space compared with the conventional dictionary. To restore more high-frequency information, the residual dictionary was introduced based on the reconstructed images with the 2D separable dictionary, which captured the high-frequency information of edges, angles and corners in images. Combining the two dictionary learning procedures into one framework, the proposed method was expected to synthesize high resolution images with high quality. The proposed algorithm was tested on public natural image set. The experiment results show that the proposed image super-resolution method based on 2D separable dictionary and residual dictionary is effective and superior.

     

    • FullText(HTML)
  • loading
    • References(20)
  • [1]
    PARK S, PARK M, KANG M.Super-resolution image reconstruction: a technical overview[J]. IEEE Signal Processing Magazine, 2003, 20(3): 21-36.
    [2]
    YANG J, HUANG T.Super-resolution imaging[M]. Boca Raton: CRC Press, 2010: 3-35.
    [3]
    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.
    [4]
    TAKEDA H, FARSIU S, MILANFAR P.Kernel regression for image processing and reconstruction[J]. IEEE Transactions on Image Processing, 2007, 15(2): 349-366.
    [5]
    FAUSIU 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.
    [6]
    TIPPING M E, BISHOP C M.Bayesian image super-resolution[J]. Advances in Neural Information Processing Systems, 2002, 15: 1303-1310.
    [7]
    FATTAL R. Image upsampling via imposed edge statistics[J]. ACM Transactions on Graphics, 2007, 26(3): Article 95.
    [8]
    CHANG H, YEUNG D, XIONG Y.Super-resolution through neighbor embedding[C]//IEEE Conference on Computer Vision and Pattern Recognition. Washington, D C: IEEE, 2004: 275-282.
    [9]
    SUN J, XU Z, SHUM H.Image super-resolution using gradient profile prior[C]//IEEE Conference on Computer Vision and Pattern Recognition. Anchorage: IEEE, 2008: 1-8.
    [10]
    FERNANDEZ-GRANDA C, CANDES E J.Super-resolution via transform-invariant group-sparse regularizetion[C]//IEEE International Conference on Computer Vision. Sydney: IEEE, 2013: 3336-3343.
    [11]
    YANG J, WRIGHT J, HUANG T, et al.Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing, 2010, 19(11): 2861-2873.
    [12]
    GAO X, ZHANG K, TAO D, et al.Joint learning for single-image super-resolution via a coupled constraint[J]. IEEE Transactions on Image Processing, 2012, 21(2): 469-480.
    [13]
    YANG J, WANG Z, LIN Z, et al.Coupled dictionary training for image super resolution[J]. IEEE Transactions on Image Processing, 2012, 21(8): 3467-3478.
    [14]
    YANG M, YANG C.Fast direct superresolution by simple functions[C]//IEEE International Conference on Computer Vision. Sydney: IEEE, 2013: 561-568.
    [15]
    ZHANG K, TAO D, GAO X, et al.Learning multiple linear mappings for efficient single image super-resolution[J]. IEEE Transactions on Image Processing, 2015, 24(3): 846-861.
    [16]
    WANG L, WU H, PAN C.Fast image upsampling via the displacement field[J]. IEEE Transactions on Image Processing, 2014, 23(12): 5123-5135.
    [17]
    HAWE S, SEIBERT M, KLEINSTEUBER M.Separable dictionary learning[C]//IEEE International Conference on Computer Vision and Pattern Recognition. Portland: IEEE, 2013, 9(4): 438-445.
    [18]
    CAIAFA C, CICHOCKI A.Computing sparse representations of multidimensional signals using Kronecker bases[J]. Neural Computation, 2013, 25(1): 186-220.
    [19]
    ZHANGJZHAOCXIONGRet al.Image super-resolution via dual-dictionary learning and sparse representationIEEE International Symposium on Circuits and Systems. Seoul: IEEE201216881691

    ZHANG J, ZHAO C, XIONG R, et al.Image super-resolution via dual-dictionary learning and sparse representation[C]//IEEE International Symposium on Circuits and Systems. Seoul: IEEE, 2012: 1688-1691.
    [20]
    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.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    Figures(5)  / Tables(1)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views(83) PDF downloads(0) Cited by()
    Proportional views
    Related

    Copyright © 2009《 石油钻探技术 》 All Rights Reserved.

    Address：North-Star Times Tower, No.8 Beichendong Road, Chaoyang District, Beijing, China China Pos：100101

    Tel：010-84988317，84988356，84988357 Fax：021-64253812 Email：syzt@vip.163.com

    Beijing public network security equipment 11010502036328　Beijing ICP backup 17033152

    Supported by: Beijing Renhe Information Technology Co., Ltd.  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return