11,991 research outputs found
Blind deconvolution of medical ultrasound images: parametric inverse filtering approach
©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2007.910179The problem of reconstruction of ultrasound images by means of blind deconvolution has long been recognized as one of the central problems in medical ultrasound imaging. In this paper, this problem is addressed via proposing a blind deconvolution method which is innovative in several ways. In particular, the method is based on parametric inverse filtering, whose parameters are optimized using two-stage processing. At the first stage, some partial information on the point spread function is recovered. Subsequently, this information is used to explicitly constrain the spectral shape of the inverse filter. From this perspective, the proposed methodology can be viewed as a ldquohybridizationrdquo of two standard strategies in blind deconvolution, which are based on either concurrent or successive estimation of the point spread function and the image of interest. Moreover, evidence is provided that the ldquohybridrdquo approach can outperform the standard ones in a number of important practical cases. Additionally, the present study introduces a different approach to parameterizing the inverse filter. Specifically, we propose to model the inverse transfer function as a member of a principal shift-invariant subspace. It is shown that such a parameterization results in considerably more stable reconstructions as compared to standard parameterization methods. Finally, it is shown how the inverse filters designed in this way can be used to deconvolve the images in a nonblind manner so as to further improve their quality. The usefulness and practicability of all the introduced innovations are proven in a series of both in silico and in vivo experiments. Finally, it is shown that the proposed deconvolution algorithms are capable of improving the resolution of ultrasound images by factors of 2.24 or 6.52 (as judged by the autocorrelation criterion) depending on the type of regularization method used
Deconvolution with Shapelets
We seek to find a shapelet-based scheme for deconvolving galaxy images from
the PSF which leads to unbiased shear measurements. Based on the analytic
formulation of convolution in shapelet space, we construct a procedure to
recover the unconvolved shapelet coefficients under the assumption that the PSF
is perfectly known. Using specific simulations, we test this approach and
compare it to other published approaches. We show that convolution in shapelet
space leads to a shapelet model of order
with and being the maximum orders of the intrinsic
galaxy and the PSF models, respectively. Deconvolution is hence a
transformation which maps a certain number of convolved coefficients onto a
generally smaller number of deconvolved coefficients. By inferring the latter
number from data, we construct the maximum-likelihood solution for this
transformation and obtain unbiased shear estimates with a remarkable amount of
noise reduction compared to established approaches. This finding is
particularly valid for complicated PSF models and low images, which
renders our approach suitable for typical weak-lensing conditions.Comment: 9 pages, 9 figures, submitted to A&
The non-coplanar baselines effect in radio interferometry: The W-Projection algorithm
We consider a troublesome form of non-isoplanatism in synthesis radio
telescopes: non-coplanar baselines. We present a novel interpretation of the
non-coplanar baselines effect as being due to differential Fresnel diffraction
in the neighborhood of the array antennas.
We have developed a new algorithm to deal with this effect. Our new
algorithm, which we call "W-projection", has markedly superior performance
compared to existing algorithms. At roughly equivalent levels of accuracy,
W-projection can be up to an order of magnitude faster than the corresponding
facet-based algorithms. Furthermore, the precision of result is not tightly
coupled to computing time.
W-projection has important consequences for the design and operation of the
new generation of radio telescopes operating at centimeter and longer
wavelengths.Comment: Accepted for publication in "IEEE Journal of Selected Topics in
Signal Processing
Non-local image deconvolution by Cauchy sequence
We present the deconvolution between two smooth function vectors as a Cauchy
sequence of weight functions. From this we develop a Taylor series expansion of
the convolution problem that leads to a non-local approximation for the
deconvolution in terms of continuous function spaces. Optimisation of this form
against a given measure of error produces a theoretically more exact algorithm.
The discretization of this formulation provides a deconvolution iteration that
deconvolves images quicker than the Richardson-Lucy algorithm.Comment: 12 pages, 3 figure
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