19 research outputs found
GPU-Accelerated Algorithms for Compressed Signals Recovery with Application to Astronomical Imagery Deblurring
Compressive sensing promises to enable bandwidth-efficient on-board
compression of astronomical data by lifting the encoding complexity from the
source to the receiver. The signal is recovered off-line, exploiting GPUs
parallel computation capabilities to speedup the reconstruction process.
However, inherent GPU hardware constraints limit the size of the recoverable
signal and the speedup practically achievable. In this work, we design parallel
algorithms that exploit the properties of circulant matrices for efficient
GPU-accelerated sparse signals recovery. Our approach reduces the memory
requirements, allowing us to recover very large signals with limited memory. In
addition, it achieves a tenfold signal recovery speedup thanks to ad-hoc
parallelization of matrix-vector multiplications and matrix inversions.
Finally, we practically demonstrate our algorithms in a typical application of
circulant matrices: deblurring a sparse astronomical image in the compressed
domain
Image Restoration for Remote Sensing: Overview and Toolbox
Remote sensing provides valuable information about objects or areas from a
distance in either active (e.g., RADAR and LiDAR) or passive (e.g.,
multispectral and hyperspectral) modes. The quality of data acquired by
remotely sensed imaging sensors (both active and passive) is often degraded by
a variety of noise types and artifacts. Image restoration, which is a vibrant
field of research in the remote sensing community, is the task of recovering
the true unknown image from the degraded observed image. Each imaging sensor
induces unique noise types and artifacts into the observed image. This fact has
led to the expansion of restoration techniques in different paths according to
each sensor type. This review paper brings together the advances of image
restoration techniques with particular focuses on synthetic aperture radar and
hyperspectral images as the most active sub-fields of image restoration in the
remote sensing community. We, therefore, provide a comprehensive,
discipline-specific starting point for researchers at different levels (i.e.,
students, researchers, and senior researchers) willing to investigate the
vibrant topic of data restoration by supplying sufficient detail and
references. Additionally, this review paper accompanies a toolbox to provide a
platform to encourage interested students and researchers in the field to
further explore the restoration techniques and fast-forward the community. The
toolboxes are provided in https://github.com/ImageRestorationToolbox.Comment: This paper is under review in GRS
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Parallelisation of greedy algorithms for compressive sensing reconstruction
Compressive Sensing (CS) is a technique which allows a signal to be compressed at the same
time as it is captured. The process of capturing and simultaneously compressing the signal is
represented as linear sampling, which can encompass a variety of physical processes or signal
processing. Instead of explicitly identifying redundancies in the source signal, CS relies on the
property of sparsity in order to reconstruct the compressed signal. While linear sampling is
much less burdensome than conventional compression, this is more than made up for by the high
computational cost of reconstructing a signal which has been captured using CS. Even when
using some of the fastest reconstruction techniques, known as greedy pursuits, reconstruction
of large problems can pose a significant burden, consuming a great deal of memory as well as
compute time.
Parallel computing is the foundation of the field of High Performance Computing (HPC).
Modern supercomputers are generally composed of large clusters of standard servers, with a
dedicated low-latency high-bandwidth interconnect network. On such a cluster, an appropriately
written program can harness vast quantities of memory and computational power. However, in
order to exploit a parallel compute resource, an algorithm usually has to be redesigned from
the ground up. In this thesis I describe the development of parallel variants of two algorithms
commonly used in CS reconstruction, Matching Pursuit (MP) and Orthogonal Matching Pursuit
(OMP), resulting in the new distributed compute algorithms DistMP and DistOMP. I present
the results from experiments showing how DistMP and DistOMP can utilise a compute cluster
to solve CS problems much more quickly than a single computer could alone. Speed-up of as
much as a factor of 76 is observed with DistMP when utilising 210 workers across 14 servers,
compared to a single worker. Finally, I demonstrate how DistOMP can solve a problem with a
429GB equivalent sampling matrix in as little as 62 minutes using a 16-node compute cluster.Funded by an ICASE award from the Engineering and Physical Sciences Research Council, with sponsorship provided by Thales Research and Technology
Sparse linear regression from perturbed data
The problem of sparse linear regression is relevant in the context of linear
system identification from large datasets. When data are collected from
real-world experiments, measurements are always affected by perturbations or
low-precision representations. However, the problem of sparse linear regression
from fully-perturbed data is scarcely studied in the literature, due to its
mathematical complexity. In this paper, we show that, by assuming bounded
perturbations, this problem can be tackled by solving low-complex l2 and l1
minimization problems. Both theoretical guarantees and numerical results are
illustrated in the paper
Robust density modelling using the student's t-distribution for human action recognition
The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE
Foundations, Inference, and Deconvolution in Image Restoration
Image restoration is a critical preprocessing step in computer vision,
producing images with reduced noise, blur, and pixel defects.
This enables precise higher-level reasoning as to the scene content in
later stages of the vision pipeline (e.g., object segmentation,
detection, recognition, and tracking).
Restoration techniques have found extensive usage in a broad range of
applications from industry, medicine, astronomy, biology, and
photography.
The recovery of high-grade results requires models of the image
degradation process, giving rise to a class of often heavily
underconstrained, inverse problems.
A further challenge specific to the problem of blur removal is noise
amplification, which may cause strong distortion by ringing artifacts.
This dissertation presents new insights and problem solving procedures
for three areas of image restoration, namely (1) model
foundations, (2) Bayesian inference for high-order Markov
random fields (MRFs), and (3) blind image deblurring
(deconvolution).
As basic research on model foundations, we contribute to reconciling
the perceived differences between probabilistic MRFs on the one hand,
and deterministic variational models on the other.
To do so, we restrict the variational functional to locally supported finite
elements (FE) and integrate over the domain.
This yields a sum of terms depending locally on FE basis coefficients,
and by identifying the latter with pixels, the terms resolve to MRF
potential functions.
In contrast with previous literature, we place special emphasis on robust
regularizers used commonly in contemporary computer vision.
Moreover, we draw samples from the derived models to further
demonstrate the probabilistic connection.
Another focal issue is a class of high-order Field of Experts MRFs
which are learned generatively from natural image data and yield
best quantitative results under Bayesian estimation.
This involves minimizing an integral expression, which has no closed
form solution in general.
However, the MRF class under study has Gaussian mixture potentials,
permitting expansion by indicator variables as a technical measure.
As approximate inference method, we study Gibbs sampling in the
context of non-blind deblurring and obtain excellent results, yet
at the cost of high computing effort.
In reaction to this, we turn to the mean field algorithm, and show
that it scales quadratically in the clique size for a standard
restoration setting with linear degradation model.
An empirical study of mean field over several restoration scenarios
confirms advantageous properties with regard to both image quality and
computational runtime.
This dissertation further examines the problem of blind deconvolution,
beginning with localized blur from fast moving objects in the
scene, or from camera defocus.
Forgoing dedicated hardware or user labels, we rely only on the image
as input and introduce a latent variable model to explain the
non-uniform blur.
The inference procedure estimates freely varying kernels and we
demonstrate its generality by extensive experiments.
We further present a discriminative method for blind removal of camera
shake.
In particular, we interleave discriminative non-blind deconvolution
steps with kernel estimation and leverage the error cancellation
effects of the Regression Tree Field model to attain a deblurring
process with tightly linked sequential stages
Computational imaging and automated identification for aqueous environments
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2011Sampling the vast volumes of the ocean requires tools capable of observing from a distance while retaining detail necessary for biology and ecology, ideal for optical methods.
Algorithms that work with existing SeaBED AUV imagery are developed, including habitat classi fication with bag-of-words models and multi-stage boosting for rock sh detection.
Methods for extracting images of sh from videos of longline operations are demonstrated.
A prototype digital holographic imaging device is designed and tested for quantitative
in situ microscale imaging. Theory to support the device is developed, including particle
noise and the effects of motion. A Wigner-domain model provides optimal settings and
optical limits for spherical and planar holographic references.
Algorithms to extract the information from real-world digital holograms are created.
Focus metrics are discussed, including a novel focus detector using local Zernike moments.
Two methods for estimating lateral positions of objects in holograms without reconstruction
are presented by extending a summation kernel to spherical references and using a local
frequency signature from a Riesz transform. A new metric for quickly estimating object
depths without reconstruction is proposed and tested. An example application, quantifying
oil droplet size distributions in an underwater plume, demonstrates the efficacy of the
prototype and algorithms.Funding was provided by NOAA Grant #5710002014, NOAA NMFS Grant #NA17RJ1223, NSF Grant #OCE-0925284, and NOAA Grant #NA10OAR417008