86 research outputs found
On multi-degree splines
Multi-degree splines are piecewise polynomial functions having sections of
different degrees. For these splines, we discuss the construction of a B-spline
basis by means of integral recurrence relations, extending the class of
multi-degree splines that can be derived by existing approaches. We then
propose a new alternative method for constructing and evaluating the B-spline
basis, based on the use of so-called transition functions. Using the transition
functions we develop general algorithms for knot-insertion, degree elevation
and conversion to B\'ezier form, essential tools for applications in geometric
modeling. We present numerical examples and briefly discuss how the same idea
can be used in order to construct geometrically continuous multi-degree
splines
Space-variant Generalized Gaussian Regularization for Image Restoration
We propose a new space-variant regularization term for variational image
restoration based on the assumption that the gradient magnitudes of the target
image distribute locally according to a half-Generalized Gaussian distribution.
This leads to a highly flexible regularizer characterized by two per-pixel free
parameters, which are automatically estimated from the observed image. The
proposed regularizer is coupled with either the or the fidelity
terms, in order to effectively deal with additive white Gaussian noise or
impulsive noises such as, e.g, additive white Laplace and salt and pepper
noise. The restored image is efficiently computed by means of an iterative
numerical algorithm based on the alternating direction method of multipliers.
Numerical examples indicate that the proposed regularizer holds the potential
for achieving high quality restorations for a wide range of target images
characterized by different gradient distributions and for the different types
of noise considered
Quantum median filter for total variation image denoising
In this new computing paradigm, named quantum computing, researchers from all over
the world are taking their first steps in designing quantum circuits for image process-
ing, through a difficult process of knowledge transfer. This effort is named quantum
image processing, an emerging research field pushed by powerful parallel comput-
ing capabilities of quantum computers. This work goes in this direction and proposes
the challenging development of a powerful method of image denoising, such as the
total variation (TV) model, in a quantum environment. The proposed quantum TV is
described and its sub-components are analysed. Despite the natural limitations of the
current capabilities of quantum devices, the experimental results show a competitive
denoising performance compared to the classical variational TV counterpar
A general framework for nonlinear regularized Krylov-based image restoration
Abstract. This paper introduces a new approach to computing an approximate solution of Tikhonov-regularized large-scale ill-posed problems with a general nonlinear regularization operator. The iterative method applies a sequence of projections onto generalized Krylov subspaces using a semi-implicit approach to deal with the nonlinearity in the regularization term. A suitable value of the regularization parameter is determined by the discrepancy principle. Computed examples illustrate the performance of the method applied to the restoration of blurred and noisy images
Glacial‐interglacial variations in sediment organic carbon accumulation and benthic foraminiferal assemblages on the Bermuda Rise (ODP Site 1063) during MIS 13 to 10
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94866/1/palo1807.pd
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