Award Date


Degree Type


Degree Name

Master of Science (MS)


Mathematical Sciences

First Committee Member

David G. Costa

Number of Pages



In the last two decades plenty of research has been carried out in the field of Wavelet theory and it is well known that wavelets can efficiently deal with point-like singularities. Unfortunately, such is not the case for higher dimensions singularities. To overcome this weakness of the Wavelet transform E. Candes and D. Donoho [4] introduced a new wavelet-like transform that can effectively deal with linear singularities in two dimensions, namely the Ridgelet transform. This new representation tool exploits the ability of wavelets to deal with point singularities. In fact, the Ridgelet transform is equivalent to a one-dimensional wavelet transform in the Radon domain. By doing so, a line singularity is transformed into a point singularity (by means of the Radon transform) which can then be efficiently analyzed by the wavelet transform; This thesis presents the Ridgelet transform, its properties and connections to the Radon and Wavelet transform. Also, the reader is presented with practical results that allow us to see how the Ridgelet transform is much better suited than the Wavelet transform for representing images with straight edges (linear singularities).


Linear; Objects; Promising; Represent; Ridgelets; Singularities; Transforms; Wavelet

Controlled Subject


File Format


File Size

2437.12 KB

Degree Grantor

University of Nevada, Las Vegas




If you are the rightful copyright holder of this dissertation or thesis and wish to have the full text removed from Digital Scholarship@UNLV, please submit a request to and include clear identification of the work, preferably with URL.


IN COPYRIGHT. For more information about this rights statement, please visit