natural signals are continues, therefore, digitizing is an essential task enabling us to use computing tools to process them. according to the nyquist/shannon sampling theory, the sampling frequency must be at least twice the maximum frequency contained in the signal which is being sampled; otherwise, some high frequencies may be aliased and result in a bad reconstruction. the nyquist sampling ...

The Digital Curvelet Transform provides near-optimal reconstruction of twice-continuously differentiable (C) curves. Previous implementations of the algorithm have not exploited newer technologies in modern processors, including the MMX and SSE instruction sets. Various optimization techniques were used on a form of the Digital Curvelet Transform resulting in the fastest Curvelet Transform curr...

Glossary WT1D The one-dimensional Wavelet Transform as defined in [1]. See also [2] in this volume. WT2D The two-dimensional Wavelet Transform. Discrete Ridgelet Trasnform (DRT) The discrete implementation of the continuous Ridgelet transform. Fast Slant Stack (FSS) An algebraically exact Radon transform of data on a Cartesian grid. First Generation Discrete Curvelet Transform (DCTG1) The discr...

SURE-LET Approach is used for reducing or removing noise in brain Magnetic Resonance Images (MRI). Removing or reducing noise is an active research area in image processing. Rician noise is the dominant noise in MRIs. Due to this type of noise, the abnormal tissue (cancerous tissue) may be misclassified as normal tissue and introduces bias into MRI measurements that can have significant impact ...

The images usually bring different kinds of noise in the process of receiving, coding and transmission. In this paper the Curvelet transform is used for de-noising of image. Two digital implementations of the Curvelet transform (a multiscale transform) viz the Unequally Spaced Fast Fourier Transform (USFFT) and the Wrapping Algorithm are used to de-noise images degraded by different types of no...

We reconsider the continuous curvelet transform from a signal processing point of view. We show that the analyzing elements of the curvelet transform, the curvelets, can be understood as analytic signals in the sense of the partial Hilbert transform. We then generalize the usual curvelets by the monogenic curvelets, which are analytic signals in the sense of the Riesz transform. They yield a ne...

A digital image watermarking algorithm based on fast curvelet transform is proposed. Firstly, the carrier image is decomposed by fast curvelet transform, and, the watermarking image is scrambled by Arnold transform. Secondly, the binary watermarking image is embedded into the medium frequency coefficients according to the human visual characteristics and curvelet coefficients. Experiment result...

We present in this paper a new method for con­ trast enhancement based on the curvelet transform. The curvelet transform represents edges better than wavelets, and is therefore well-suited for multiscale edge enhancement. We compare this approach with enhancement based on the wavelet transform, and the Multiscale Retinex. In a range of examples, we use edge de­ tection and segmentation, among o...