Enhanced Image Super-Resolution Technique Using Convolutional Neural Network

  • Kah Keong Chua
  • Yong Haur Tay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8237)


A framework for image super resolution using Convolutional Neural Network (CNN) was developed. In this paper, we focus on verifying the performance of Convolutional Neural Network compared to other methods. CNN generally outperforms other super resolution methods. The training images were collected from various categories, i.e. flowers, buildings, animals, vehicles, human and cuisine. The neural network trained with multiple categories of training images made the CNN more robust towards different test scenarios. Common image degradation, i.e. motion blur and noise, can be reduced when the CNN is provided with proper training samples.


Image Super Resolution Convolutional Neural Network motion blur image denoising 


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  1. 1.
    Asuni, N., Giachetti, A.: Accuracy Improvements and Artifacts Removal in Edge Based Image Interpolation. In: Proc. 3rd Int. Conf. Comput. Vis. Theory Appl. (VISAPP), pp. 58–65 (2008)Google Scholar
  2. 2.
    Giachetti, A., Asuni, N.: Real-Time Artifact-Free Image Upscaling. IEEE Transactions on Image Proccessing 20(10), 2760–2768 (2011)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Tsai, R.Y., Huang, T.S.: Multiframe Image Restoration and Registration. JAI Press Inc., London (1984)Google Scholar
  4. 4.
    Tian, J., Ma, K.-K.: A Survey on Super-Resolution Imaging. Springer-Verlag London (2011)Google Scholar
  5. 5.
    Torrieri, D., Bakhru, K.: Neural Network Superresolution. IEEE, 1594–1598 (1997)Google Scholar
  6. 6.
    Egmont-Petersen, M., Ridder, D., Handels, H.: Image Processing with Neural Networks - A Review. Pattern Recognition 35, 2279–2301 (2002)CrossRefzbMATHGoogle Scholar
  7. 7.
    Sherrod, P.: DTREG,
  8. 8.
    Chen, M.J., Huang, C.H., Lee, W.L.: A Fast Edge-Oriented Algorithm for Digital Images. Image Vis. Comput. 20, 805–812 (2002)CrossRefGoogle Scholar
  9. 9.
    Elad, M., Fueur, A.: Restoration of a SIngle Super-Resolution Image from Several BLurred, Noise and Undersampled Measured Images. IEEE Transactions on Image Processing 6(12), 1646–1658 (1997)CrossRefGoogle Scholar
  10. 10.
    Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based Super-Resolution. IEEE Computer Graphics and Applications 22(2), 56–65 (2002)CrossRefGoogle Scholar
  11. 11.
    Sudheer Babu, R., Sreenivasa Murthy, K.E.: A Survey on the Methods of Super-Resolution Image Reconstruction. International Journal of Computer Application 15(2) (February 2011)Google Scholar
  12. 12.
    Liu, H.Y., Zhang, Y.S., Ji, S.: Study in the Methods of Super-Resolution Image Reconstruction. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVII(B2) (2008)Google Scholar
  13. 13.
    Baker, S., Kanade, T.: Limits on Super Resolution and How to Break Them. IEEE TPAMI 24(9), 1167–1183 (2002)CrossRefGoogle Scholar
  14. 14.
  15. 15.
    The SSIM Index for Image Quality Assessment,

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Kah Keong Chua
    • 1
  • Yong Haur Tay
    • 1
  1. 1.Centre for Computing and Intelligent SystemsUniversiti Tunku Abdul RahmanKuala LumpurMalaysia

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