4,864 research outputs found
Iterative CT reconstruction using shearlet-based regularization
In computerized tomography, it is important to reduce the image noise without increasing the acquisition dose. Extensive research has been done into total variation minimization for image denoising and sparse-view reconstruction. However, TV minimization methods show superior denoising performance for simple images (with little texture), but result in texture information loss when applied to more complex images. Since in medical imaging, we are often confronted with textured images, it might not be beneficial to use TV. Our objective is to find a regularization term outperforming TV for sparse-view reconstruction and image denoising in general. A recent efficient solver was developed for convex problems, based on a split-Bregman approach, able to incorporate regularization terms different from TV. In this work, a proof-of-concept study demonstrates the usage of the discrete shearlet transform as a sparsifying transform within this solver for CT reconstructions. In particular, the regularization term is the 1-norm of the shearlet coefficients. We compared our newly developed shearlet approach to traditional TV on both sparse-view and on low-count simulated and measured preclinical data. Shearlet-based regularization does not outperform TV-based regularization for all datasets. Reconstructed images exhibit small aliasing artifacts in sparse-view reconstruction problems, but show no staircasing effect. This results in a slightly higher resolution than with TV-based regularization
Wavelet/shearlet hybridized neural networks for biomedical image restoration
Recently, new programming paradigms have emerged that combine parallelism and numerical computations with algorithmic differentiation. This approach allows for the hybridization of neural network techniques for inverse imaging problems with more traditional methods such as wavelet-based sparsity modelling techniques. The benefits are twofold: on the one hand traditional methods with well-known properties can be integrated in neural networks, either as separate layers or tightly integrated in the network, on the other hand, parameters in traditional methods can be trained end-to-end from datasets in a neural network "fashion" (e.g., using Adagrad or Adam optimizers). In this paper, we explore these hybrid neural networks in the context of shearlet-based regularization for the purpose of biomedical image restoration. Due to the reduced number of parameters, this approach seems a promising strategy especially when dealing with small training data sets
Finding faint HI structure in and around galaxies: scraping the barrel
Soon to be operational HI survey instruments such as APERTIF and ASKAP will
produce large datasets. These surveys will provide information about the HI in
and around hundreds of galaxies with a typical signal-to-noise ratio of
10 in the inner regions and 1 in the outer regions. In addition, such
surveys will make it possible to probe faint HI structures, typically located
in the vicinity of galaxies, such as extra-planar-gas, tails and filaments.
These structures are crucial for understanding galaxy evolution, particularly
when they are studied in relation to the local environment. Our aim is to find
optimized kernels for the discovery of faint and morphologically complex HI
structures. Therefore, using HI data from a variety of galaxies, we explore
state-of-the-art filtering algorithms. We show that the intensity-driven
gradient filter, due to its adaptive characteristics, is the optimal choice. In
fact, this filter requires only minimal tuning of the input parameters to
enhance the signal-to-noise ratio of faint components. In addition, it does not
degrade the resolution of the high signal-to-noise component of a source. The
filtering process must be fast and be embedded in an interactive visualization
tool in order to support fast inspection of a large number of sources. To
achieve such interactive exploration, we implemented a multi-core CPU (OpenMP)
and a GPU (OpenGL) version of this filter in a 3D visualization environment
().Comment: 17 pages, 9 figures, 4 tables. Astronomy and Computing, accepte
ARKCoS: Artifact-Suppressed Accelerated Radial Kernel Convolution on the Sphere
We describe a hybrid Fourier/direct space convolution algorithm for compact
radial (azimuthally symmetric) kernels on the sphere. For high resolution maps
covering a large fraction of the sky, our implementation takes advantage of the
inexpensive massive parallelism afforded by consumer graphics processing units
(GPUs). Applications involve modeling of instrumental beam shapes in terms of
compact kernels, computation of fine-scale wavelet transformations, and optimal
filtering for the detection of point sources. Our algorithm works for any
pixelization where pixels are grouped into isolatitude rings. Even for kernels
that are not bandwidth limited, ringing features are completely absent on an
ECP grid. We demonstrate that they can be highly suppressed on the popular
HEALPix pixelization, for which we develop a freely available implementation of
the algorithm. As an example application, we show that running on a high-end
consumer graphics card our method speeds up beam convolution for simulations of
a characteristic Planck high frequency instrument channel by two orders of
magnitude compared to the commonly used HEALPix implementation on one CPU core
while maintaining at typical a fractional RMS accuracy of about 1 part in 10^5.Comment: 10 pages, 6 figures. Submitted to Astronomy and Astrophysics.
Replaced to match published version. Code can be downloaded at
https://github.com/elsner/arkco
Distributed and parallel sparse convex optimization for radio interferometry with PURIFY
Next generation radio interferometric telescopes are entering an era of big
data with extremely large data sets. While these telescopes can observe the sky
in higher sensitivity and resolution than before, computational challenges in
image reconstruction need to be overcome to realize the potential of
forthcoming telescopes. New methods in sparse image reconstruction and convex
optimization techniques (cf. compressive sensing) have shown to produce higher
fidelity reconstructions of simulations and real observations than traditional
methods. This article presents distributed and parallel algorithms and
implementations to perform sparse image reconstruction, with significant
practical considerations that are important for implementing these algorithms
for Big Data. We benchmark the algorithms presented, showing that they are
considerably faster than their serial equivalents. We then pre-sample gridding
kernels to scale the distributed algorithms to larger data sizes, showing
application times for 1 Gb to 2.4 Tb data sets over 25 to 100 nodes for up to
50 billion visibilities, and find that the run-times for the distributed
algorithms range from 100 milliseconds to 3 minutes per iteration. This work
presents an important step in working towards computationally scalable and
efficient algorithms and implementations that are needed to image observations
of both extended and compact sources from next generation radio interferometers
such as the SKA. The algorithms are implemented in the latest versions of the
SOPT (https://github.com/astro-informatics/sopt) and PURIFY
(https://github.com/astro-informatics/purify) software packages {(Versions
3.1.0)}, which have been released alongside of this article.Comment: 25 pages, 5 figure
- …