Discrete and Stationary Wavelet Decomposition for IMAGE Resolution Enhancement

  ijett-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2013 by IJETT Journal
Volume-4 Issue-7                      
Year of Publication : 2013
Authors : B Siva Kumar , S Nagaraj


B Siva Kumar , S Nagaraj. "Discrete and Stationary Wavelet Decomposition for IMAGE Resolution Enhancement ". International Journal of Engineering Trends and Technology (IJETT). V4(7):2885-2889 Jul 2013. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group.


An image resolution enhancement technique based on interpolation of the high frequency subband images obtained by discrete wavelet transform (DWT) and the input image. The edges are enhanced by introducing an intermediate stage by using stationary wavelet transform (SWT). DWT is applied in order to decompose an input image into different subbands. Then the high frequency subbands as well as the input image are interpolated. The estimated high frequency subbands are being modified by using high frequency subband obtained throug h SWT. Then all these subbands are combined to generate a new high resolution image by using inverse DWT (IDWT). The quantitative and visual results are showing the superiority of the proposed technique over the conventional and state - of - art image resoluti on enhancement techniques.


[1] L. Yi - bo, X. Hong, and Z. Sen - yue, “The wrinkle generation method for facial reconstruction based on extraction of partition wrinkle line features and fractal interpolation ,” in Proc. 4th Int. Conf. Image Graph. , Aug. 22 – 24, 2007, pp. 933 – 937.
[2] Y. Rener, J. Wei, and C. Ken, “Downsample - based multiple description coding and post - processing of decoding,” in Proc. 27th Chinese Control Conf. , Jul. 16 – 18, 2008, pp. 253 – 256.
[3] H. Demirel, G. Anbarjafari, and S. Izadpanahi, “Improved motionbased localized super resolution technique using discrete wavelet transform for low resolution video enhancement,” in Proc. 17 th Eur. Signal Process. Conf. , Glasgow, Scotland, Aug. 2009, pp. 1097 – 1101.
[4] Y. Piao, I. Shin, and H. W. Park, “Image resolution enhancement using inter - subband correlation in wavelet domain,” in Proc. Int. Conf. Image Process. , 2007, vol. 1, pp. I - 445 – 448.
[5] H. Demirel and G. Anbarjafari, “Satellite image resolution enha ncement using complex wavelet transform,” IEEE Geoscience and Remote Sensing Letter , vol. 7, no. 1, pp. 123 – 126, Jan. 2010.
[6] C. B. Atkins, C. A. Bouman, and J. P. Allebach, “Optimal image scaling using pixel classification,” in Proc. Int. Conf. Image Proce ss. , Oct. 7 – 10, 2001, vol. 3, pp. 864 – 867.
[7] W. K. Carey, D. B. Chuang, and S. S. Hemami, “Regularity - preserving image interpolation,” IEEE Trans. Image Process. , vol. 8, no. 9, pp. 1295 – 1297, Sep. 1999.
[8] S. Mallat , A Wavelet Tour of Signal Processing , 2nd ed. New York: Academic, 1999.
[9] J. E. Fowler, “The redundant discrete wavelet transform and additive noise,”Mississippi State ERC, Mississippi State University, Tech. Rep. MSSU - COE - ERC - 04 - 04, Mar. 2004.
[10] X. Li and M. T. Orchard, “New edge - directed interpolation,” IEEE Trans. Image Process. , vol. 10, no. 10, pp. 1521 – 1527, Oct. 2001.
[11] K. Kinebuchi, D. D. Muresan, and R. G. Baraniuk, “Waveletbased statistical signal processing using hidden Markov models,” in Proc. Int. Conf. Acoust., Speech, Signal Process. , 2001, vol. 3, pp. 7 – 11.
[12] S. Zhao, H. Han, and S. Peng, “Wavelet domain HMT - based image super resolution,” in Proc. IEEE Int. Conf. Image Process. , Sep. 2003, vol. 2, pp. 933 – 936.
[13] A. Temizel and T. Vlachos, “Wavelet domain image resolution enhancement using cycle - spinning,” Electron. Lett. , vol. 41, no. 3, pp. 119 – 121, Feb. 3, 2005.
[14] A. Temizel and T. Vlachos, “Image resolution upscaling in the wavelet domain using directional cycle spinning,” J. Electron. Imag. , vol. 14, no. 4, 2005.
[15] G. Anbarjafari and H. Demirel, “Image super resolution based on int erpolation of wavelet domain high frequency subbands and the spatial domain input image,” ETRI J. , vol. 32, no. 3, pp. 390 – 394, Jun. 2010.

Discrete wavelet transform, image super resolution, stationary wavelet transform.