Image Resolution Enhancement based on Curvelet Transform

Zehira Haddad, Adrien Chan Hon Tong, Jaime Lopez Krahe

Abstract

We present an image resolution enhancement method based on Curvelet transform. This transform is used to decompose the input image into different subbands. After this decomposition, a nonlinear function is applied to the Curvelet coefficients in order to enhance the content of the different frequency subbands. These enhanced frequency subbands are then interpolated. We increase the enhancement results by a fusion of the obtained data and the interpolated input image. An image database is used for experiments. The visual results are showing the superiority of the proposed technique compared to two state-of-art image resolution enhancement techniques. These results have been confirmed by quantitative image quality metrics.

References

  1. Suganya. P, Mohanapriya. N, Vanitha. A, 2013, Survey on Image Resolution Techniques for Satellite Images, International Journal of Computer Science and Information Technologies, Vol. 4 no.6, pp. 835-838.
  2. Abirami. A, Akshaya. N, Poornakala. D, Priyanka. D, Ram kumar. C, 2013, Enhancement of Satellite Image Resolution With Moving Objects, IOSR Journal of Electronics and Communication Engineering (IOSRJECE), Volume 4, Issue 6, PP 22-27.
  3. Harikrishna. O, Maheshwari. A, 2012, Satellite Image Resolution Enhancement using DWT Technique, International Journal of Soft Computing and Engineering (IJSCE) Volume-2, Issue-5.
  4. Dr. Muna F. Al-Samaraie , Dr. Nedhal Abdul Majied Al Saiyd, 2011, Medical colored image enhancement using wavelet Transform followed by image sharpening, Ubiquitous Computing and Communication Journal, Volume 6 Number 5.
  5. Hanan Saleh S. Ahmed and Md Jan Nordin, 2011, Improving Diagnostic Viewing of Medical Images using Enhancement Algorithms, Journal of Computer Science, vol 7 no.12, 2011.
  6. Carey. W. K., Chuang; D. B., Hemami. S. S, 1999, Regularity preserving image interpolation, IEEE Transaction on Image Processing, vol.8, no.9, pp.1293-1297.
  7. Demiral. H and Anbarjafari. G, 2010, Image super resolution based on interpolation of wavelet domain high frequency sub-bands and spatial domain input image, ETRI Journal, vol. 32, no. 3, pp.390-394.
  8. Xie. Z, 2003, A wavelet based algorithm for image super resolution, B. S, University of science and technology of China.
  9. Birare. S. D, Nalbalwar. S. L, 2010, Review on super resolution of images using wavelet transform International Journal of Engineering Science and Technology, vol.2, no. 12, pp. 7363-7371.
  10. Bagawade Ramdas, Bhagawat Keshav, Patil Pradeep, 2012, Wavelet Transform Techniques for Image Resolution Enhancement: A Study, International Journal of Emerging Technology and Advanced Engineering, Volume 2, Issue 4.
  11. Venkata ramana. S , Narayana reddy, S, 2014, A Novel Method to Improve Resolution of Satellite Images Using DWT and Interpolation, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 3, Issue 1.
  12. Hasan Demirel, Gholamreza Anbarjafari, 2011, Image Resolution Enhancement by Using Discrete and Stationary Wavelet Decomposition” IEEE Transactions On Image Processing, vol. 20, no. 5.
  13. Battula. R. V. S. Narayana, K. Nirmala, 2012, Image Resolution Enhancement by Using Stationary and Discrete Wavelet Decomposition I.J. Image, Graphics and Signal Processing, vol. 11,pp. 41-46.
  14. Tripathi. N, Gopal Kirar. K, 2014, Image Resolution Enhancement by Wavelet Transform Based Interpolation and Image Fusion”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 8, pp. 318-323.
  15. Land. E, 1986, Recent advances in retinex theory, Vis. Res., vol. 26, no. 1, pp. 7-21.
  16. Barnard. K and Funt. B, 1999, “Investigations into multiscale retinex,” in Color Imaging: Vision and Technology. New York: Wiley, pp. 9-17.
  17. Velde, K. V, 1999, “Multi-scale color image enhancement,” in Proc. Int. Conf. Image Processing, vol. 3, pp. 584-587.
  18. Starck, J., Murtagh, F., Candes, E.J., and Donnoho, D.L, 2003: Gray and color image contrast enhancement by the curvelet transform. IEEE Trans. Image Processing; 12(6):706-17.
  19. Cherifi. D, Azeddine Beghdadi, Belbachir. A. H, 2010: Color contrast enhancement method using steerable pyramid transform. Signal, Image and Video Processing 4(2): 247-262.
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Paper Citation


in Harvard Style

Haddad Z., Tong A. and Krahe J. (2017). Image Resolution Enhancement based on Curvelet Transform . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 167-173. DOI: 10.5220/0006127201670173


in Bibtex Style

@conference{visapp17,
author={Zehira Haddad and Adrien Chan Hon Tong and Jaime Lopez Krahe},
title={Image Resolution Enhancement based on Curvelet Transform},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={167-173},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006127201670173},
isbn={978-989-758-225-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - Image Resolution Enhancement based on Curvelet Transform
SN - 978-989-758-225-7
AU - Haddad Z.
AU - Tong A.
AU - Krahe J.
PY - 2017
SP - 167
EP - 173
DO - 10.5220/0006127201670173