25,942 research outputs found
Restoration of Poissonian Images Using Alternating Direction Optimization
Much research has been devoted to the problem of restoring Poissonian images,
namely for medical and astronomical applications. However, the restoration of
these images using state-of-the-art regularizers (such as those based on
multiscale representations or total variation) is still an active research
area, since the associated optimization problems are quite challenging. In this
paper, we propose an approach to deconvolving Poissonian images, which is based
on an alternating direction optimization method. The standard regularization
(or maximum a posteriori) restoration criterion, which combines the Poisson
log-likelihood with a (non-smooth) convex regularizer (log-prior), leads to
hard optimization problems: the log-likelihood is non-quadratic and
non-separable, the regularizer is non-smooth, and there is a non-negativity
constraint. Using standard convex analysis tools, we present sufficient
conditions for existence and uniqueness of solutions of these optimization
problems, for several types of regularizers: total-variation, frame-based
analysis, and frame-based synthesis. We attack these problems with an instance
of the alternating direction method of multipliers (ADMM), which belongs to the
family of augmented Lagrangian algorithms. We study sufficient conditions for
convergence and show that these are satisfied, either under total-variation or
frame-based (analysis and synthesis) regularization. The resulting algorithms
are shown to outperform alternative state-of-the-art methods, both in terms of
speed and restoration accuracy.Comment: 12 pages, 12 figures, 2 tables. Submitted to the IEEE Transactions on
Image Processin
Formation of laser-induced periodic surface structures on niobium by femtosecond laser irradiation
Multiplicative Noise Removal Using Variable Splitting and Constrained Optimization
Multiplicative noise (also known as speckle noise) models are central to the
study of coherent imaging systems, such as synthetic aperture radar and sonar,
and ultrasound and laser imaging. These models introduce two additional layers
of difficulties with respect to the standard Gaussian additive noise scenario:
(1) the noise is multiplied by (rather than added to) the original image; (2)
the noise is not Gaussian, with Rayleigh and Gamma being commonly used
densities. These two features of multiplicative noise models preclude the
direct application of most state-of-the-art algorithms, which are designed for
solving unconstrained optimization problems where the objective has two terms:
a quadratic data term (log-likelihood), reflecting the additive and Gaussian
nature of the noise, plus a convex (possibly nonsmooth) regularizer (e.g., a
total variation or wavelet-based regularizer/prior). In this paper, we address
these difficulties by: (1) converting the multiplicative model into an additive
one by taking logarithms, as proposed by some other authors; (2) using variable
splitting to obtain an equivalent constrained problem; and (3) dealing with
this optimization problem using the augmented Lagrangian framework. A set of
experiments shows that the proposed method, which we name MIDAL (multiplicative
image denoising by augmented Lagrangian), yields state-of-the-art results both
in terms of speed and denoising performance.Comment: 11 pages, 7 figures, 2 tables. To appear in the IEEE Transactions on
Image Processing
Regularity at infinity of real mappings and a Morse-Sard theorem
We prove a new Morse-Sard type theorem for the asymptotic critical values of
semi-algebraic mappings and a new fibration theorem at infinity for
mappings. We show the equivalence of three different types of regularity
conditions which have been used in the literature in order to control the
asymptotic behaviour of mappings. The central role of our picture is played by
the -regularity and its bridge toward the -regularity which implies
topological triviality at infinity
Five-Dimensional f(R) Braneworld Models
After incorporating the f(R) gravity into the general braneworld sum rules
scope, it is shown that some particular class of warped five dimensional
nonlinear braneworld models, which may be interesting for the hierarchy problem
solution, still require a negative tension brane. For other classes of warp
factors (suitable and not suitable for approaching the hierarchy problem) it is
not necessary any negative brane tension in the compactification scheme. In
this vein, it is argued that in the bulk f(R) gravity context, some types of
warp factors may be useful for approaching the hierarchy problem and for
evading the necessity of a negative brane tension in the compactification
scheme.Comment: 10 pages, references updated, small modifications. Accepted for
publication in Phys. Rev.
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