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Super-Resolution Restoration of MMW Image Using Sparse Representation Based on Couple Dictionaries

  • Li Shang
  • Yan Zhou
  • Liu Tao
  • Zhan-li Sun
Part of the Communications in Computer and Information Science book series (CCIS, volume 304)

Abstract

This paper addresses the problem of the super-resolution restoration of a single millimeter wave image using sparse representation based on couple dictionary training. Utilizing the coefficients of the sparse representation of each low-resolution image patch, the high-resolution image patches can be generated, further, the low resolution image can be reconstructed well. The quality of a restoration MMW image was measured by the relative single noise ratio (RSNR. Compared with image restoration results of bicubic, experimental results prove that the sparse representation is effective in the super-resolution restoration of MMW.

Keywords

Millimeter wave image Super-resolution Image restoration Sparse representation Couple dictionary 

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References

  1. 1.
    Matthias, H., Christoph, S.: Learning Sparse Representations by Non-negative matrix Factorization and Sequential Come Programming. Journal of Machine Learning Research 7, 1385–1407 (2006)zbMATHGoogle Scholar
  2. 2.
    Yang, J.C., Wright, J., Huang, T., et al.: Image Super-resolution Via Sparse Representation. IEEE Transactions on Image Processing 19, 2861–2873 (2010)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Rubinstein, R., Bruckstein, A.M., Elad, M., et al.: Dictionaries for Sparse Representation Modeling. IEEE Proceedings-Special Issue on Applications of Sparse Representation & Compressive Sensing 98, 1045–1057 (2010)Google Scholar
  4. 4.
    Sundareshan, M.K., Bhattacharjee, S.: Superresolution of Passive Millimeter-Wave Im-ages Using a Combined Maximum-likelihood Optimization and Projection-onto-convex-sets Approach. In: Proc. of SPIE Conf. on Passive Millimeter-wave Imaging Technology, Acrosense 2001, Orlando, FL., USA, vol. 4373, pp. 105–116 (2001)Google Scholar
  5. 5.
    Li, S.Z.: MAP Image Restoration and Segmentation by Constrained Optimization. IEEE Transactions on Image Processing 7, 1730–1735 (2002)CrossRefGoogle Scholar
  6. 6.
    Baker, S., Kanade, T.: Limits on Super-resolution and How to Break Them. IEEE Transaction on Pattern Analysis and Machine Intelligence (TPAMI) 24, 1167–1183 (2002)CrossRefGoogle Scholar
  7. 7.
    Zeyde, R., Elad, M., Protter, M.: On Single Image Scale-Up Using Sparse-Representations. In: Boissonnat, J.-D., Chenin, P., Cohen, A., Gout, C., Lyche, T., Mazure, M.-L., Schumaker, L. (eds.) Curves and Surfaces 2011. LNCS, vol. 6920, pp. 711–730. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Yang, J.C., Wright, J., Huang, T., et al.: Image Superresolution Via Sparse Representation of Raw Image Patches. In: Gjessing, S., Chepoi, V. (eds.) ECOOP 1988. LNCS, vol. 322, pp. 1–8. Springer, Heidelberg (1988)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Li Shang
    • 1
    • 2
  • Yan Zhou
    • 1
    • 3
  • Liu Tao
    • 1
  • Zhan-li Sun
    • 4
  1. 1.Department of Electronic Information EngineeringSuzhou Vocational UniversitySuzhouChina
  2. 2.Department of AutomationUniversity of Science and Technology of ChinaHefeiChina
  3. 3.School of Electronics and Information EngineeringSoochow UniversitySuzhouChina
  4. 4.College of Electrical Engineering and AutomationAnhui UniversityHefeiChina

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