The Nonsubsampled Contourlet Transform: Theory, Design, and Applications

Zhou, J.; Do, M.N.; Da Cunha, A.L.

doi:10.1109/TIP.2006.877507

ScienceOpen: research and publishing network

For Publishers

For Researchers

Blog
About

Search
Advanced search

views

recommends

Record: found
Abstract: not found
Article: not found

The Nonsubsampled Contourlet Transform: Theory, Design, and Applications

Author(s): A.L. Da Cunha , J. Zhou , M.N. Do

Publication date (Print): October 2006

Journal: IEEE Transactions on Image Processing

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Read this article at

ScienceOpenPublisher PubMed

Bookmark

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Related collections

Most cited references 32

Record: found
Abstract: not found
Article: not found

The contourlet transform: an efficient directional multiresolution image representation

M.N. Do, M. C. Vetterli (2005)

0 comments Cited 299 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Image denoising using scale mixtures of Gaussians in the wavelet domain.

J Portilla, V Strela, M.J. Wainwright … (2003)

We describe a method for removing noise from digital images, based on a statistical model of the coefficients of an overcomplete multiscale oriented basis. Neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables: a Gaussian vector and a hidden positive scalar multiplier. The latter modulates the local variance of the coefficients in the neighborhood, and is thus able to account for the empirically observed correlation between the coefficient amplitudes. Under this model, the Bayesian least squares estimate of each coefficient reduces to a weighted average of the local linear estimates over all possible values of the hidden multiplier variable. We demonstrate through simulations with images contaminated by additive white Gaussian noise that the performance of this method substantially surpasses that of previously published methods, both visually and in terms of mean squared error.