48 research outputs found
Range resolution improvement in FM-based passive radars using deconvolution
FM-based passive bistatic radar (PBR) systems suffer from low range resolution because of the low baseband bandwidth of commercial FM broadcasts. In this paper, we propose a range resolution improvement method using deconvolution. The output of the PBR matched filter is processed using a deconvolution algorithm which assumes that targets are isolated, i.e., sparse in the range domain. The deconvolution algorithm is iterative and was implemented by performing successive orthogonal projections onto supporting hyperplanes of the epigraph set of a convex cost function. Simulation examples are presented. © 2016, Springer-Verlag London
Image restoration and reconstruction using projections onto epigraph set of convex cost fuchtions
Cataloged from PDF version of article.This thesis focuses on image restoration and reconstruction problems. These
inverse problems are solved using a convex optimization algorithm based on orthogonal
Projections onto the Epigraph Set of a Convex Cost functions (PESC).
In order to solve the convex minimization problem, the dimension of the problem
is lifted by one and then using the epigraph concept the feasibility sets corresponding
to the cost function are defined. Since the cost function is a convex
function in R
N , the corresponding epigraph set is also a convex set in R
N+1. The
convex optimization algorithm starts with an arbitrary initial estimate in R
N+1
and at each step of the iterative algorithm, an orthogonal projection is performed
onto one of the constraint sets associated with the cost function in a sequential
manner. The PESC algorithm provides globally optimal solutions for different
functions such as total variation, `1-norm, `2-norm, and entropic cost functions.
Denoising, deconvolution and compressive sensing are among the applications of
PESC algorithm. The Projection onto Epigraph Set of Total Variation function
(PES-TV) is used in 2-D applications and for 1-D applications Projection onto
Epigraph Set of `1-norm cost function (PES-`1) is utilized.
In PES-`1 algorithm, first the observation signal is decomposed using wavelet
or pyramidal decomposition. Both wavelet denoising and denoising methods using
the concept of sparsity are based on soft-thresholding. In sparsity-based denoising
methods, it is assumed that the original signal is sparse in some transform domain
such as Fourier, DCT, and/or wavelet domain and transform domain coefficients
of the noisy signal are soft-thresholded to reduce noise. Here, the relationship between
the standard soft-thresholding based denoising methods and sparsity-based
wavelet denoising methods is described. A deterministic soft-threshold estimation
method using the epigraph set of `1-norm cost function is presented. It is
demonstrated that the size of the `1-ball can be determined using linear algebra.
The size of the `1-ball in turn determines the soft-threshold. The PESC, PES-TV
and PES-`1 algorithms, are described in detail in this thesis. Extensive simulation
results are presented. PESC based inverse restoration and reconstruction
algorithm is compared to the state of the art methods in the literature.Tofighi, MohammadM.S
Phase and TV Based Convex Sets for Blind Deconvolution of Microscopic Images
In this article, two closed and convex sets for blind deconvolution problem
are proposed. Most blurring functions in microscopy are symmetric with respect
to the origin. Therefore, they do not modify the phase of the Fourier transform
(FT) of the original image. As a result blurred image and the original image
have the same FT phase. Therefore, the set of images with a prescribed FT phase
can be used as a constraint set in blind deconvolution problems. Another convex
set that can be used during the image reconstruction process is the epigraph
set of Total Variation (TV) function. This set does not need a prescribed upper
bound on the total variation of the image. The upper bound is automatically
adjusted according to the current image of the restoration process. Both of
these two closed and convex sets can be used as a part of any blind
deconvolution algorithm. Simulation examples are presented.Comment: Submitted to IEEE Selected Topics in Signal Processin
Projections onto the epigraph set of the filtered variation function based deconvolution algorithm
A new deconvolution algorithm based on orthogonal projections onto the hyperplanes and the epigraph set of a convex cost function is presented. In this algorithm, the convex sets corresponding to the cost function are defined by increasing the dimension of the minimization problem by one. The Filtered Variation (FV) function is used as the convex cost function in this algorithm. Since the FV cost function is a convex function in RN, then the corresponding epigraph set is also a convex set in the lifted set in RN+1. At each step of the iterative deconvolution algorithm, starting with an arbitrary initial estimate in RN+1, first the projections onto the hyperplanes are performed to obtain the first deconvolution estimate. Then an orthogonal projection is performed onto the epigraph set of the FV cost function, in order to regularize and denoise the deconvolution estimate, in a sequential manner. The algorithm converges to the deblurred image. © 2016 IEEE
Phase and TV Based Convex Sets for Blind Deconvolution of Microscopic Images
In this paper, two closed and convex sets for blind deconvolution problem are proposed. Most blurring functions in microscopy are symmetric with respect to the origin. Therefore, they do not modify the phase of the Fourier transform (FT) of the original image. As a result blurred image and the original image have the same FT phase. Therefore, the set of images with a prescribed FT phase can be used as a constraint set in blind deconvolution problems. Another convex set that can be used during the image reconstruction process is the Epigraph Set of Total Variation (ESTV) function. This set does not need a prescribed upper bound on the Total Variation (TV) of the image. The upper bound is automatically adjusted according to the current image of the restoration process. Both the TV of the image and the blurring filter are regularized using the ESTV set. Both the phase information set and the ESTV are closed and convex sets. Therefore they can be used as a part of any blind deconvolution algorithm. Simulation examples are presented. © 2015 IEEE
Proximity Operators of Discrete Information Divergences
Information divergences allow one to assess how close two distributions are
from each other. Among the large panel of available measures, a special
attention has been paid to convex -divergences, such as
Kullback-Leibler, Jeffreys-Kullback, Hellinger, Chi-Square, Renyi, and
I divergences. While -divergences have been extensively
studied in convex analysis, their use in optimization problems often remains
challenging. In this regard, one of the main shortcomings of existing methods
is that the minimization of -divergences is usually performed with
respect to one of their arguments, possibly within alternating optimization
techniques. In this paper, we overcome this limitation by deriving new
closed-form expressions for the proximity operator of such two-variable
functions. This makes it possible to employ standard proximal methods for
efficiently solving a wide range of convex optimization problems involving
-divergences. In addition, we show that these proximity operators are
useful to compute the epigraphical projection of several functions of practical
interest. The proposed proximal tools are numerically validated in the context
of optimal query execution within database management systems, where the
problem of selectivity estimation plays a central role. Experiments are carried
out on small to large scale scenarios
Range resolution improvement in passive bistatic radars using nested FM channels and least squares approach
One of the main disadvantages of using commercial broadcasts in a Passive Bistatic Radar (PBR) system is the range resolution. Using multiple broadcast channels to improve the radar performance is offered as a solution to this problem. However, it suffers from detection performance due to the side-lobes that matched filter creates for using multiple channels. In this article, we introduce a deconvolution algorithm to suppress the side-lobes. The two-dimensional matched filter output of a PBR is further analyzed as a deconvolution problem. The deconvolution algorithm is based on making successive projections onto the hyperplanes representing the time delay of a target. Resulting iterative deconvolution algorithm is globally convergent because all constraint sets are closed and convex. Simulation results in an FM based PBR system are presented. © 2015 SPIE
Templates for Convex Cone Problems with Applications to Sparse Signal Recovery
This paper develops a general framework for solving a variety of convex cone
problems that frequently arise in signal processing, machine learning,
statistics, and other fields. The approach works as follows: first, determine a
conic formulation of the problem; second, determine its dual; third, apply
smoothing; and fourth, solve using an optimal first-order method. A merit of
this approach is its flexibility: for example, all compressed sensing problems
can be solved via this approach. These include models with objective
functionals such as the total-variation norm, ||Wx||_1 where W is arbitrary, or
a combination thereof. In addition, the paper also introduces a number of
technical contributions such as a novel continuation scheme, a novel approach
for controlling the step size, and some new results showing that the smooth and
unsmoothed problems are sometimes formally equivalent. Combined with our
framework, these lead to novel, stable and computationally efficient
algorithms. For instance, our general implementation is competitive with
state-of-the-art methods for solving intensively studied problems such as the
LASSO. Further, numerical experiments show that one can solve the Dantzig
selector problem, for which no efficient large-scale solvers exist, in a few
hundred iterations. Finally, the paper is accompanied with a software release.
This software is not a single, monolithic solver; rather, it is a suite of
programs and routines designed to serve as building blocks for constructing
complete algorithms.Comment: The TFOCS software is available at http://tfocs.stanford.edu This
version has updated reference