What is a curvelet transform?

The curvelet trans- form is a multiscale directional transform that allows an almost optimal nonadaptive sparse rep- resentation of objects with edges. It has generated increasing interest in the community of applied mathematics and signal processing over the years.

What is curvelet transform in image processing?

The curvelet transform is a multiscale directional transform that allows an almost optimal nonadaptive sparse representation of objects with edges.

What does wavelet transform do?

In contrast to STFT having equally spaced time-frequency localization, wavelet transform provides high frequency resolution at low frequencies and high time resolution at high frequencies.

What is curvelet domain?

The curvelet transform is a known tool used in the attenuation of coherent and incoherent noise in seismic data. It utilises the fact that signal and noise are usually better separated in the curvelet domain than in the time- space (TX) domain.

Which is the best tool for Curvelet transform?

A central tool is Fourier domain computation of an approximate digital random transform. In a curvelet transform, we will use sparsity and its applications [5]. In the past, we have proposed a work on novel image denoising method which is based on DCT basis and sparse representation [6].

What is the difference between Curvelet transform and wavelet transform?

Coif man and Donoho [12] pioneered in wavelet thresholding pointed out that wavelet algorithm exhibits visual artefacts‟. Curvelet transform is a multi scale transform with strong directional character in which elements are highly anisotropic at fine Scales. The

How is wavelet transform used in image denoising?

Wavelet transform enable us to represent signals with a high degree of scarcity. This is the principle behind a non-linear wavelet based signal estimation technique known as wavelet denoising. In this paper we explore wavelet denoising of images using several thresholding techniques such as SURE SHRINK, VISU SHRINK and BAYES SHRINK.