2,661 research outputs found
Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation
Copyright @ 2011 Shadi AlZubi et al. This article has been made available through the Brunel Open Access Publishing Fund.The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying region of interest (ROI) in medical images which are obtained from different medical scanners such as PET, CT, or MRI. Multiresolution analysis (MRA) using wavelet, ridgelet, and curvelet transforms has been used in the proposed segmentation system. It is particularly a challenging task to classify cancers in human organs in scanners output using shape or gray-level information; organs shape changes throw different slices in medical stack and the gray-level intensity overlap in soft tissues. Curvelet transform is a new extension of wavelet and ridgelet transforms which aims to deal with interesting phenomena occurring along curves. Curvelet transforms has been tested on medical data sets, and results are compared with those obtained from the other transforms. Tests indicate that using curvelet significantly improves the classification of abnormal tissues in the scans and reduce the surrounding noise
Sparse image reconstruction on the sphere: analysis and synthesis
We develop techniques to solve ill-posed inverse problems on the sphere by
sparse regularisation, exploiting sparsity in both axisymmetric and directional
scale-discretised wavelet space. Denoising, inpainting, and deconvolution
problems, and combinations thereof, are considered as examples. Inverse
problems are solved in both the analysis and synthesis settings, with a number
of different sampling schemes. The most effective approach is that with the
most restricted solution-space, which depends on the interplay between the
adopted sampling scheme, the selection of the analysis/synthesis problem, and
any weighting of the l1 norm appearing in the regularisation problem. More
efficient sampling schemes on the sphere improve reconstruction fidelity by
restricting the solution-space and also by improving sparsity in wavelet space.
We apply the technique to denoise Planck 353 GHz observations, improving the
ability to extract the structure of Galactic dust emission, which is important
for studying Galactic magnetism.Comment: 11 pages, 6 Figure
Blind Curvelet based Denoising of Seismic Surveys in Coherent and Incoherent Noise Environments
The localized nature of curvelet functions, together with their frequency and
dip characteristics, makes the curvelet transform an excellent choice for
processing seismic data. In this work, a denoising method is proposed based on
a combination of the curvelet transform and a whitening filter along with
procedure for noise variance estimation. The whitening filter is added to get
the best performance of the curvelet transform under coherent and incoherent
correlated noise cases, and furthermore, it simplifies the noise estimation
method and makes it easy to use the standard threshold methodology without
digging into the curvelet domain. The proposed method is tested on
pseudo-synthetic data by adding noise to real noise-less data set of the
Netherlands offshore F3 block and on the field data set from east Texas, USA,
containing ground roll noise. Our experimental results show that the proposed
algorithm can achieve the best results under all types of noises (incoherent or
uncorrelated or random, and coherent noise)
Wavelet transforms and their applications to MHD and plasma turbulence: a review
Wavelet analysis and compression tools are reviewed and different
applications to study MHD and plasma turbulence are presented. We introduce the
continuous and the orthogonal wavelet transform and detail several statistical
diagnostics based on the wavelet coefficients. We then show how to extract
coherent structures out of fully developed turbulent flows using wavelet-based
denoising. Finally some multiscale numerical simulation schemes using wavelets
are described. Several examples for analyzing, compressing and computing one,
two and three dimensional turbulent MHD or plasma flows are presented.Comment: Journal of Plasma Physics, 201
Exact reconstruction with directional wavelets on the sphere
A new formalism is derived for the analysis and exact reconstruction of
band-limited signals on the sphere with directional wavelets. It represents an
evolution of the wavelet formalism developed by Antoine & Vandergheynst (1999)
and Wiaux et al. (2005). The translations of the wavelets at any point on the
sphere and their proper rotations are still defined through the continuous
three-dimensional rotations. The dilations of the wavelets are directly defined
in harmonic space through a new kernel dilation, which is a modification of an
existing harmonic dilation. A family of factorized steerable functions with
compact harmonic support which are suitable for this kernel dilation is firstly
identified. A scale discretized wavelet formalism is then derived, relying on
this dilation. The discrete nature of the analysis scales allows the exact
reconstruction of band-limited signals. A corresponding exact multi-resolution
algorithm is finally described and an implementation is tested. The formalism
is of interest notably for the denoising or the deconvolution of signals on the
sphere with a sparse expansion in wavelets. In astrophysics, it finds a
particular application for the identification of localized directional features
in the cosmic microwave background (CMB) data, such as the imprint of
topological defects, in particular cosmic strings, and for their reconstruction
after separation from the other signal components.Comment: 22 pages, 2 figures. Version 2 matches version accepted for
publication in MNRAS. Version 3 (identical to version 2) posted for code
release announcement - "Steerable scale discretised wavelets on the sphere" -
S2DW code available for download at
http://www.mrao.cam.ac.uk/~jdm57/software.htm
Image interpolation using Shearlet based iterative refinement
This paper proposes an image interpolation algorithm exploiting sparse
representation for natural images. It involves three main steps: (a) obtaining
an initial estimate of the high resolution image using linear methods like FIR
filtering, (b) promoting sparsity in a selected dictionary through iterative
thresholding, and (c) extracting high frequency information from the
approximation to refine the initial estimate. For the sparse modeling, a
shearlet dictionary is chosen to yield a multiscale directional representation.
The proposed algorithm is compared to several state-of-the-art methods to
assess its objective as well as subjective performance. Compared to the cubic
spline interpolation method, an average PSNR gain of around 0.8 dB is observed
over a dataset of 200 images
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