Robust Contrast And Resolution Enhancement Of Images Using Multiwavelets And SVD

  ijett-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2013 by IJETT Journal
Volume-4 Issue-7                      
Year of Publication : 2013
Authors : Mr.D.Rakesh , Mr.M.Sreenivasulu , Mr.T.Chakrapani , Mr.K.Sudhakar

Citation 

Mr.D.Rakesh , Mr.M.Sreenivasulu , Mr.T.Chakrapani , Mr.K.Sudhakar. "Robust Contrast And Resolution Enhancement Of Images Using Multiwavelets And SVD". International Journal of Engineering Trends and Technology (IJETT). V4(7):3216-3221 Jul 2013. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group.

Abstract

Resolution and contrast are the two important attributes of an image. In this paper we developed a method to enhance the quality of the given image. The enhancement is done both with respect to resolut ion and contrast. The proposed technique uses DWT, SWT and SVD. To increase the resolution, the proposed method uses DWT and SWT. These transforms decompose the given image into four sub - bands, out of which one is of low frequency and the rest are of high frequency. The HF components are interpolated using conventional interpolation techniques. Then we use IDWT to combine the interpolated high frequency and low frequency components. To increase the contrast, we use SVD and DWT. The experimental results sh ow that proposed technique gives good re sults over conventional methods for both colour and gray scale images

References

[1] Hasan Demirel and Gholamreza Anbarjafari, “IMAGE Re solution Enhancement by Using Discrete and Stationary Wavelet Decomposition” IEEE transactions on IMAGE PROCESSING,VOL. 20, NO. 5
[2] H. Demirel and G. Anbarjafari, “Satellite image resolution enhancement using complex wavelet transform,” IEEE Geosciences and Remote Sensing Letter , vol. 7, no. 1, pp. 123 – 126, Jan. 2010.
[3] Kirk Baker,” Singular Value Decomposition Tutorial ”. March 29, 2005
[4] Y. Piao, I. Shin, and H. W. Park, “Image resolution enhancement using inter - sub - band correlation in wavelet domai n,” in Proc. Int. Conf. Image Process., 2007, vol. 1, pp. I - 445 – 448.
[5] J. W. Wang and W. Y. Chen, ‘‘Eye detection based on head contour geometry and wavelet sub - band projection,’’ Opt. Eng., vol. 45, no. 5, pp. 057001 - 1 -- 057001 - 12, May 2006.
[6] J. L. St arck, E. J. Candes, and D. L. Donoho, ‘‘The curvelet transform for image de - noising,’’ IEEE Trans. Image Process., vol. 11, no. 6, pp. 670 -- 684, Jun. 2002.
[7] C. C. Liu, D. Q. Dai, and H. Yan, ‘‘Local discriminant wavelet packet coordinates for face reco gnition,’’ J. Mach. Learn. Res., vol. 8, pp. 1165 -- 1195, 2007.
[8] M. Lamard, W. Daccache, G. Cazuguel, C. Roux, and B. Cochener, ‘‘Use of a JPEG - 2000 wavelet compression scheme forcontent - based ophthalmologic retinal images retrieval,’’ in Proc. 27th IEE E EMBS, 2005, pp. 4010 -- 4013.

Keywords
Discrete wavelet transform (DWT), Singular value decomposition (SVD), Stationary wavelet transform (SWT).