2,517 research outputs found
A multiscale regularized restoration algorithm for XMM-Newton data
We introduce a new multiscale restoration algorithm for images with few
photons counts and its use for denoising XMM data. We use a thresholding of the
wavelet space so as to remove the noise contribution at each scale while
preserving the multiscale information of the signal. Contrary to other
algorithms the signal restoration process is the same whatever the signal to
noise ratio is. Thresholds according to a Poisson noise process are indeed
computed analytically at each scale thanks to the use of the unnormalized Haar
wavelet transform. Promising preliminary results are obtained on X-ray data for
Abell 2163 with the computation of a temperature map.Comment: To appear in the Proceedings of `Galaxy Clusters and the High
Redshift Universe Observed in X-rays', XXIth Moriond Astrophysics Meeting
(March 2001), Eds. Doris Neumann et a
An Improved Approach for Contrast Enhancement of Spinal Cord Images based on Multiscale Retinex Algorithm
This paper presents a new approach for contrast enhancement of spinal cord
medical images based on multirate scheme incorporated into multiscale retinex
algorithm. The proposed work here uses HSV color space, since HSV color space
separates color details from intensity. The enhancement of medical image is
achieved by down sampling the original image into five versions, namely, tiny,
small, medium, fine, and normal scale. This is due to the fact that the each
versions of the image when independently enhanced and reconstructed results in
enormous improvement in the visual quality. Further, the contrast stretching
and MultiScale Retinex (MSR) techniques are exploited in order to enhance each
of the scaled version of the image. Finally, the enhanced image is obtained by
combining each of these scales in an efficient way to obtain the composite
enhanced image. The efficiency of the proposed algorithm is validated by using
a wavelet energy metric in the wavelet domain. Reconstructed image using
proposed method highlights the details (edges and tissues), reduces image noise
(Gaussian and Speckle) and improves the overall contrast. The proposed
algorithm also enhances sharp edges of the tissue surrounding the spinal cord
regions which is useful for diagnosis of spinal cord lesions. Elaborated
experiments are conducted on several medical images and results presented show
that the enhanced medical pictures are of good quality and is found to be
better compared with other researcher methods.Comment: 13 pages, 6 figures, International Journal of Imaging and Robotics.
arXiv admin note: text overlap with arXiv:1406.571
CT Image Reconstruction by Spatial-Radon Domain Data-Driven Tight Frame Regularization
This paper proposes a spatial-Radon domain CT image reconstruction model
based on data-driven tight frames (SRD-DDTF). The proposed SRD-DDTF model
combines the idea of joint image and Radon domain inpainting model of
\cite{Dong2013X} and that of the data-driven tight frames for image denoising
\cite{cai2014data}. It is different from existing models in that both CT image
and its corresponding high quality projection image are reconstructed
simultaneously using sparsity priors by tight frames that are adaptively
learned from the data to provide optimal sparse approximations. An alternative
minimization algorithm is designed to solve the proposed model which is
nonsmooth and nonconvex. Convergence analysis of the algorithm is provided.
Numerical experiments showed that the SRD-DDTF model is superior to the model
by \cite{Dong2013X} especially in recovering some subtle structures in the
images
Multiplicative Noise Removal Using L1 Fidelity on Frame Coefficients
We address the denoising of images contaminated with multiplicative noise,
e.g. speckle noise. Classical ways to solve such problems are filtering,
statistical (Bayesian) methods, variational methods, and methods that convert
the multiplicative noise into additive noise (using a logarithmic function),
shrinkage of the coefficients of the log-image data in a wavelet basis or in a
frame, and transform back the result using an exponential function. We propose
a method composed of several stages: we use the log-image data and apply a
reasonable under-optimal hard-thresholding on its curvelet transform; then we
apply a variational method where we minimize a specialized criterion composed
of an data-fitting to the thresholded coefficients and a Total
Variation regularization (TV) term in the image domain; the restored image is
an exponential of the obtained minimizer, weighted in a way that the mean of
the original image is preserved. Our restored images combine the advantages of
shrinkage and variational methods and avoid their main drawbacks. For the
minimization stage, we propose a properly adapted fast minimization scheme
based on Douglas-Rachford splitting. The existence of a minimizer of our
specialized criterion being proven, we demonstrate the convergence of the
minimization scheme. The obtained numerical results outperform the main
alternative methods
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
Weak Lensing Mass Reconstruction using Wavelets
This paper presents a new method for the reconstruction of weak lensing mass
maps. It uses the multiscale entropy concept, which is based on wavelets, and
the False Discovery Rate which allows us to derive robust detection levels in
wavelet space. We show that this new restoration approach outperforms several
standard techniques currently used for weak shear mass reconstruction. This
method can also be used to separate E and B modes in the shear field, and thus
test for the presence of residual systematic effects. We concentrate on large
blind cosmic shear surveys, and illustrate our results using simulated shear
maps derived from N-Body Lambda-CDM simulations with added noise corresponding
to both ground-based and space-based observations.Comment: Accepted manuscript with all figures can be downloaded at:
http://jstarck.free.fr/aa_wlens05.pdf and software can be downloaded at
http://jstarck.free.fr/mrlens.htm
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