Undecimated Wavelet Transform Based Contrast Enhancement
Authors: Numan Unaldi, Samil Temel, Süleyman Demirci
Abstract:
A novel undecimated wavelet transform based contrast enhancement algorithmis proposed to for both gray scale andcolor images. Contrast enhancement is realized by tuning the magnitude of approximation coefficients at each level with respect to the approximation coefficients of one higher level during the inverse transform phase in a center/surround enhancement sense.The performance of the proposed algorithm is evaluated using a statistical visual contrast measure (VCM). Experimental results on the proposed algorithm show improvement in terms of the VCM.
Keywords: Image enhancement, local contrast enhancement, visual contrast measure.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1087822
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[1] K. V. Velde, “Multi-scale color image enhancement,” Proc. Intl. Conf. Image Processing, vol. 3, pp. 584-587, 1999.
[2] L. Chen, C. Chen, and K. Parker, “Adaptive feature enhancement for mammographic images with multi-resolution analysis,” Journal of Electronic Imaging, pp. 467-478, 1997.
[3] J. Fu, H. Lien, and S. Wong, “Wavelet-based HEQ of gastric sonogram images,” Comp. Med. Imaging and Graph., pp. 59-68, 2000.
[4] T. Reeves, and M. Jernigan, “Multiscale-based image enhancement,” Canadian Conf. on Elect. and Comp. Engineering, pp. 500-505, 1997.
[5] B. Peng, W. Fu, and C. Yang, “Contrast enhancement of radiographs using shift invariant wavelet transform,” Wuhan Uni. J. of Nat. Sciences, pp. 59-62, 2000.
[6] J. L. Starck, F. Murtagh, E. J. Candes, and D. L. Donoho, “Gray and color image contrast enhancement by the curvelet transform,” IEEE Trans. on Image Processing, vol. 12, pp. 706-717, 2003.
[7] J. Qin and M. R. El-Sakka “A New Wavelet-based Method for Contrast/Edge Enhancement,” IEEEProc. Intl. Conf. on Image Processing, vol. 2, pp. 397-400, 2003.
[8] G. Fan and W. Cham, “Model-Based Edge Reconstruction for Low Bit- Rate Wavelet-Compressed Images,” IEEE Trans. Circ.&Syst. for Vid.Tech., vol. 10, no.1, pp. 120-132, 2000.
[9] P. J. Beek, P. J. L. Van, “Edge-Based Image Representation and Coding,” Ph.D. dissert., Delft Uni. of Technology, 1995.
[10] N. Unaldi, K. V. Asari, and Z. Rahman, “Fast and robust waveletbased dynamic range compression and contrast enhancement with color restoration,”Visual Information Processing XVII, Proc.SPIEvol. 7341, pp. 278-280, 2009.
[11] E. Land, “An alternative technique for the computation of the designator in the retinex theory of color vision," Proc. Nat. Acad. Sci. 83, pp. 3078-3080, 1986.
[12] Z.Rahman, D. Jobson, and G. A. Woodell, “Retinex Processing for Automatic Image Enhancement”, Journal of Electronic Imaging, 2004.
[13] J. L. Starck, J. Fadili, F. Murtagh, “The undecimated wavelet decomposition and its reconstruction,” IEEE Trans. on Image Processing, vol. 16(2), pp. 297-309, 2007.
[14] P. Dutilleux, “An implementation of the “algorithme à trous” to compute the wavelet transform,” Proc.Wavelets: Time-Frequency Methods and Phase-Space, pp. 298-304, 1989.
[15] M. J. Shensa, “Discrete wavelet transforms: Wedding the à trous and Mallat algorithms,” IEEE Trans. on Signal Processing, vol. 40, no. 10, pp. 2464–2482, 1992.
[16] D. Jobson, Z.Rahman, and G. A. Woodell, G.D.Hines, “A Comparison of Visual Statistics for the Image Enhancement of FORESITE Aerial Images with Those of Major Image Classes”, Visual Information Processing XV, Proc.SPIE6246, 2006.