http://iet.metastore.ingenta.com
1887

Single-image super-resolution using online kernel adaptive filters

Single-image super-resolution using online kernel adaptive filters

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The online kernel adaptive filters are non-linear filters which provide impulse response and are more efficient compared to other kernel algorithms. The performance of kernel adaptive filters depends on dictionary size. Here the single-image super-resolution using online kernel adaptive filters is a learning-based method. The algorithm generates a sparser solution for obtaining high-resolution image from a low-resolution image. It finds out a dictionary with most significant set of basis vectors using the spatial similarity among the dictionaries created from the low-resolution and high-resolution image patches in the training set. The dictionary is utilised to generate the high-resolution image. The algorithm is analysed on three different kernel adaptive filters, extended kernel recursive least squares, kernel recursive least squares tracker and naive online regularised risk minimisation algorithm. The performance of the super-resolution method is evaluated on a large number of images and is compared with the state-of-the art non-linear solutions to the super-resolution. The results show a better progress in peak signal-to-noise ratio up to 1.2 dB.

References

    1. 1)
      • 1. Rhee, S., Kang, M.G.: ‘Discrete cosine transform based regularized high-resolution image reconstruction algorithm’, Opt. Eng., 1999, 38, pp. 13481356.
    2. 2)
      • 2. Nguyen, N., Milanfar, P.: ‘A wavelet-based interpolation-restoration method for superresolution (wavelet superresolution)’, Circuits Syst. Signal Process., 2000, 19, (4), pp. 321338.
    3. 3)
      • 3. Ben-Ezra, M., Zomet, A., Nayar, S.K.: ‘Video super-resolution using controlled subpixel detector shifts’, IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, (6), pp. 977987.
    4. 4)
      • 4. Dai, S., Han, M., Xu, W., et al: ‘Softcuts: a soft edge smoothness prior for color image super-resolution’. IEEE Trans. Image Process., 2009, 18, (5), pp. 969981.
    5. 5)
      • 5. Patti, A.J., Altunbasak, Y.: ‘Artifact reduction for set theoretic super resolution image reconstruction with edge adaptive constraints and higher-order interpolants’, IEEE Trans. Image Process., 2001, 10, (1), pp. 179186.
    6. 6)
      • 6. Schultz, R.R., Stevenson, R.L.: ‘A Bayesian approach to image expansion for improved definition’, IEEE Trans. Image Process., 1994, 3, (3), pp. 233242.
    7. 7)
      • 7. Hardie, R.C., Barnard, K.J., Armstrong, E.E.: ‘Join MAP registration and high resolution image estimation using a sequence of under sampled images’, IEEE Trans. Image Process., 1997, 6, (12), pp. 16211633.
    8. 8)
      • 8. Freeman, W.T., Pasztor, E., Carmichael, O.: ‘Learning low-level vision’, Int. J. Comput. Vis., 2000, 40, (1), pp. 2547.
    9. 9)
      • 9. Fattal, R.: ‘Image upsampling via imposed edge statistics’, ACM Trans. Graph., 2007, 26, (3), pp. 95:195:8.
    10. 10)
      • 10. Chang, H., Yeung, D.Y., Xiong, Y.: ‘Super-resolution through neighbor embedding’. Proc. Int. IEEE Conf. CVPR, Washington D.C, USA, 2004, vol. 1, pp. 275282.
    11. 11)
      • 11. Sun, J., Zheng, N.N., Tao, H., et al: ‘Image hallucination with primal sketch priors’. Proc. Int. IEEE Conf. Computer Vision and Pattern Recognition, Madison, USA, 2003, Vol. 2, pp. 729736.
    12. 12)
      • 12. Timofte, R., De Smet, V., Van Gool, L.: ‘A + : adjusted anchored neighborhood regression for fast super-resolution’. Computer Vision, Heidelberg, Germany, 2014, pp. 111126.
    13. 13)
      • 13. Timofte, R., De Smet, V., Van Gool, L.: ‘Anchored neighborhood regression for fast example-based super-resolution’. Proc. IEEE Int. Conf. Comput. Vis., Sydney, Australia, December 2013, pp. 19201927.
    14. 14)
      • 14. Cruz, C., Mehta, R., Katkovnik, V., et al: ‘Single image super-resolution based on Wiener filter in similarity domain’, IEEE Trans. Image Process., 2018, 27, (3), pp. 13761389.
    15. 15)
      • 15. Kim, J., Lee, J.K., Lee, K.M.: ‘Deeply-recursive convolutional network for image super-resolution’. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Las Vegas, USA, June 2016, pp. 16461654.
    16. 16)
      • 16. Ming, C., Cheng, W., Jonathan, L.: ‘Single-image super-resolution in RGB space via group sparse representation’, IET Image Process., 2015, 9, pp. 461467.
    17. 17)
      • 17. Liu, D., Wang, Z., Wen, B., et al: ‘Robust single image super-resolution via deep networks with sparse prior’, IEEE Trans. Image Process., 2016, 25, (7), pp. 31943207.
    18. 18)
      • 18. Kim, J., Lee, J. K., Lee, K. M.: ‘Accurate image super-resolution using very deep convolutional networks’. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Las Vegas, USA, June 2016, pp. 16461654.
    19. 19)
      • 19. Ni, K.S., Nguyen, T.Q.: ‘Image super resolution using support vector regression’. IEEE Trans. Image Process., 2007, 16, (6), pp. 15961610.
    20. 20)
      • 20. Jesna, A., Abdulla, P.: ‘Single-image super-resolution using kernel recursive least squares’, Signal Image Video Process., 2016, 10, (8), pp. 15511558.
    21. 21)
      • 21. Fisher, I.: ‘Pattern recognition algorithms for symbol strings’, PhD thesis, Department of Computer Architecture, University of Tubingen, 2003.
    22. 22)
      • 22. Liu, W., Prıncipe, J., Haykin, S.: ‘Kernel adaptive filtering: a comprehensive introduction’ (Wiley, New York, 2010).
    23. 23)
      • 23. Liu, W., Park, I.M., Wang, Y., et al: ‘Extended kernel recursive least squares algorithm’, IEEE Trans. Signal Process., 2009, 57, pp. 38013814.
    24. 24)
      • 24. Van, V., Lazaro-Gredilla, S., Santamaria, M.I.: ‘Kernel recursive least-squares tracker for time-varying regression’, IEEE Trans. Neural Netw. Learn. Syst., 2012, 23, pp. 13131326.
    25. 25)
      • 25. Kivinen, J., Smola, A.J., Williamson, R.C.: ‘Online learning with kernels’, IEEE Trans. Signal Process., 2004, 52, (8), pp. 21652176.
    26. 26)
      • 26. Van, V., Santamar Ignacio, S.: ‘A comparative study of kernel adaptive filtering algorithms’, IEEE Digital Signal Processing (DSP) Workshop and IEEE Signal Processing Education (SPE), Napa, California, USA, 2013, Software: http://sourceforge.net/projects/kafbox/.
    27. 27)
      • 27. Li, X., Orchard, M.T.: ‘New edge-directed interpolation’, IEEE Trans. Image Process., 2001, 10, (10), pp. 15211527.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2018.5319
Loading

Related content

content/journals/10.1049/iet-ipr.2018.5319
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address