34,497 research outputs found
Efficient Bayesian-based Multi-View Deconvolution
Light sheet fluorescence microscopy is able to image large specimen with high
resolution by imaging the sam- ples from multiple angles. Multi-view
deconvolution can significantly improve the resolution and contrast of the
images, but its application has been limited due to the large size of the
datasets. Here we present a Bayesian- based derivation of multi-view
deconvolution that drastically improves the convergence time and provide a fast
implementation utilizing graphics hardware.Comment: 48 pages, 20 figures, 1 table, under review at Nature Method
Four-dimensional tomographic reconstruction by time domain decomposition
Since the beginnings of tomography, the requirement that the sample does not
change during the acquisition of one tomographic rotation is unchanged. We
derived and successfully implemented a tomographic reconstruction method which
relaxes this decades-old requirement of static samples. In the presented
method, dynamic tomographic data sets are decomposed in the temporal domain
using basis functions and deploying an L1 regularization technique where the
penalty factor is taken for spatial and temporal derivatives. We implemented
the iterative algorithm for solving the regularization problem on modern GPU
systems to demonstrate its practical use
TomograPy: A Fast, Instrument-Independent, Solar Tomography Software
Solar tomography has progressed rapidly in recent years thanks to the
development of robust algorithms and the availability of more powerful
computers. It can today provide crucial insights in solving issues related to
the line-of-sight integration present in the data of solar imagers and
coronagraphs. However, there remain challenges such as the increase of the
available volume of data, the handling of the temporal evolution of the
observed structures, and the heterogeneity of the data in multi-spacecraft
studies.
We present a generic software package that can perform fast tomographic
inversions that scales linearly with the number of measurements, linearly with
the length of the reconstruction cube (and not the number of voxels) and
linearly with the number of cores and can use data from different sources and
with a variety of physical models: TomograPy
(http://nbarbey.github.com/TomograPy/), an open-source software freely
available on the Python Package Index. For performance, TomograPy uses a
parallelized-projection algorithm. It relies on the World Coordinate System
standard to manage various data sources. A variety of inversion algorithms are
provided to perform the tomographic-map estimation. A test suite is provided
along with the code to ensure software quality. Since it makes use of the
Siddon algorithm it is restricted to rectangular parallelepiped voxels but the
spherical geometry of the corona can be handled through proper use of priors.
We describe the main features of the code and show three practical examples
of multi-spacecraft tomographic inversions using STEREO/EUVI and STEREO/COR1
data. Static and smoothly varying temporal evolution models are presented.Comment: 21 pages, 6 figures, 5 table
Phase Retrieval with Application to Optical Imaging
This review article provides a contemporary overview of phase retrieval in
optical imaging, linking the relevant optical physics to the information
processing methods and algorithms. Its purpose is to describe the current state
of the art in this area, identify challenges, and suggest vision and areas
where signal processing methods can have a large impact on optical imaging and
on the world of imaging at large, with applications in a variety of fields
ranging from biology and chemistry to physics and engineering
Accelerated High-Resolution Photoacoustic Tomography via Compressed Sensing
Current 3D photoacoustic tomography (PAT) systems offer either high image
quality or high frame rates but are not able to deliver high spatial and
temporal resolution simultaneously, which limits their ability to image dynamic
processes in living tissue. A particular example is the planar Fabry-Perot (FP)
scanner, which yields high-resolution images but takes several minutes to
sequentially map the photoacoustic field on the sensor plane, point-by-point.
However, as the spatio-temporal complexity of many absorbing tissue structures
is rather low, the data recorded in such a conventional, regularly sampled
fashion is often highly redundant. We demonstrate that combining variational
image reconstruction methods using spatial sparsity constraints with the
development of novel PAT acquisition systems capable of sub-sampling the
acoustic wave field can dramatically increase the acquisition speed while
maintaining a good spatial resolution: First, we describe and model two general
spatial sub-sampling schemes. Then, we discuss how to implement them using the
FP scanner and demonstrate the potential of these novel compressed sensing PAT
devices through simulated data from a realistic numerical phantom and through
measured data from a dynamic experimental phantom as well as from in-vivo
experiments. Our results show that images with good spatial resolution and
contrast can be obtained from highly sub-sampled PAT data if variational image
reconstruction methods that describe the tissues structures with suitable
sparsity-constraints are used. In particular, we examine the use of total
variation regularization enhanced by Bregman iterations. These novel
reconstruction strategies offer new opportunities to dramatically increase the
acquisition speed of PAT scanners that employ point-by-point sequential
scanning as well as reducing the channel count of parallelized schemes that use
detector arrays.Comment: submitted to "Physics in Medicine and Biology
- …