Now showing items 1-7 of 7
Multiscale Manifold Representation and Modeling
Many real world data sets can be viewed as points in a higher-dimensional space that lie concentrated around a lower-dimensional manifold structure. We propose a new multiscale representation for such point clouds based ...
Bayesian Tree-Structured Image Modeling
Wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and image processing. The hidden Markov tree (HMT) model captures the key features of the joint statistics of the wavelet coefficients ...
Multiscale Image Segmentation using Wavelet-domain Hidden Markov Models
We introduce a new image texture segmentation algorithm, HMTseg, based on wavelets and the hidden Markov tree (HMT) model. The HMT is a tree-structured probabilistic graph that captures the statistical properties of the ...
Wavelet-Domain Approximation and Compression of Piecewise Smooth Images
The wavelet transform provides a sparse representation for smooth images, enabling efficient approximation and compression using techniques such as zerotrees. Unfortunately, this sparsity does not extend to piecewise smooth ...
Wavelet Statistical Models and Besov Spaces
Quaternion Wavelets for Image Analysis and Processing
Using the concepts of two-dimensional Hubert transform and analytic signal, we construct a new quaternion wavelet transform (QWT). The QWT forms a tight frame and can be efficiently computed using a-2-D dual-tree filter ...
Multiple wavelet basis image denoising using Besov ball projections
We propose a new image denoising algorithm that exploits an image's representation in multiple wavelet domains. Besov balls are convex sets of images whose Besov norms are bounded from above by their radii. Projecting an ...