Undecimated Wavelet Transform Based Contrast Enhancement
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:65112", author = "Numan Unaldi and Samil Temel and Süleyman Demirci", title = "Undecimated Wavelet Transform Based Contrast Enhancement", 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.", volume = "7", number = "9", pages = "1215-4", }
