Biomedical Engineering Reference
In-Depth Information
[46] Koren, I., A Multi-Scale Spline Derivative-Based Transform for Image
Fusion and Enhancement, Ph.D. Thesis, Electrical Engineering, Uni-
versity of Florida, 1996.
[47] Kalifa, J., Laine, A., and Esser, P., Regularization in tomographic recon-
struction using thresholding estimators, IEEE Trans. Med. Imaging,
Vol. 22, No. 3, pp. 351-359, 2003.
[48] Selesnick, I., The slantlet transform, IEEE Trans. Signal Process.,
Vol. 47, No. 5, pp. 1304-1313, 1999.
[49] Candes, E. and Donoho, D., Curvelets—a surprisingly effective non-
adaptive representation for objects with edges, In: Curve and Surface
Fitting: Saint-Malo 1999, A. Cohen, C. Rabut, and L. Schumaker, Eds.,
Vanderbilt University Press, Nashville, TN, 1999.
[50] Starck, J., Candes, E., and Donoho, D., The curvelet transform for
image de-noising, IEEE Trans. Image Process., Vol. 11, No. 6, pp. 670-
684, 2002.
[51] Candes, E. and Donoho, D., Ridgelets: The key to higher-dimensional
intermittency?, Phil. Trans. R. Soc. A, Vol. 357, pp. 2495-2509, 1999.
[52] Liebling, M., Blu, T., and Unser, M., Fresnelets: New Multiresolution
Wavelet Bases for Digital Holography, IEEE Trans. Image Process.,
Vol. 12, No. 1, pp. 29-43, 2003.
[53] Gao, H., Wavelet shrinkage de-noising using the non-negative Garrote,
J. Comput. Graph. Stat., Vol. 7, pp. 469-488, 1998.
[54] Antoniadis, A. and Fan, J., Regularization of wavelet approximations,
J. Am. Stat. Assoc., Vol. 96, No. 455, pp. 939-967, 2001.
[55] Nason, G., Wavelet shrinkage using cross-validation, J. R. Stat. Soc.,
Vol. 58, pp. 463-479, 1996.
[56] Weyrich, N. and Warhola, G., De-noising using wavelets and cross-
validation, NATA Adv. Study Inst., Vol. 454, pp. 523-532, 1995.
[57] Jansen, M., Malfait, M., and Bultheel, A., Generalised cross-validation
for wavelet thresholding, Signal Process., Vol. 56, pp. 33-44, 1997.
