Add to ORKGThe authors present several innovations in a method for monotonic reconstructions. It is based on the application of constrained minimization techniques for the imposition of monotonicity on a reconstruction. In addition, they present extensions of several classical TVD limiters to a genuinely multidimensional setting. In this case the linear least squares reconstruction method is expanded upon. They also clarify data dependent weighting techniques used with the minimization process
The total variation (TV) model is attractive in that it is able to preserve sharp attributes in imag...
The research reported in this dissertation addresses the reconstruction of signals and images from l...
International audienceThis work focuses on several optimization problems involved in recovery of spa...
Exploiting sparsity in the image gradient magnitude has proved to be an effective means for reducing...
We deal with the shape reconstruction of inclusions in elastic bodies. For solving this inverse prob...
The main contribution of this paper is presenting a flexible solution to the box-constrained least s...
We study here a classical image denoising technique introduced by L. Rudin and S. Osher a few years ...
ABSTRACT: Multiple undersampled images of a scene are often obtained by using a charge-coupled devic...
We deal with the shape reconstruction of inclusions in elastic bodies. For solving this inverse prob...
In this paper, we consider the inverse problem of restoring an unknown signal or image, knowing the ...
For the constrained minimization of convex or non-convex functionals on the basis of multilevel or d...
The reconstructing of an image from its projections is formulated and solved as a constraint optimiz...
Image restoration problems are often solved by finding the minimizer of a suitable objective functio...
this paper, I will describe a computational approach to fitting cost and profit functions by the met...
Statistical reconstruction algorithms in transmission to-mography yield improved images relative to ...
The total variation (TV) model is attractive in that it is able to preserve sharp attributes in imag...
The research reported in this dissertation addresses the reconstruction of signals and images from l...
International audienceThis work focuses on several optimization problems involved in recovery of spa...
Exploiting sparsity in the image gradient magnitude has proved to be an effective means for reducing...
We deal with the shape reconstruction of inclusions in elastic bodies. For solving this inverse prob...
The main contribution of this paper is presenting a flexible solution to the box-constrained least s...
We study here a classical image denoising technique introduced by L. Rudin and S. Osher a few years ...
ABSTRACT: Multiple undersampled images of a scene are often obtained by using a charge-coupled devic...
We deal with the shape reconstruction of inclusions in elastic bodies. For solving this inverse prob...
In this paper, we consider the inverse problem of restoring an unknown signal or image, knowing the ...
For the constrained minimization of convex or non-convex functionals on the basis of multilevel or d...
The reconstructing of an image from its projections is formulated and solved as a constraint optimiz...
Image restoration problems are often solved by finding the minimizer of a suitable objective functio...
this paper, I will describe a computational approach to fitting cost and profit functions by the met...
Statistical reconstruction algorithms in transmission to-mography yield improved images relative to ...
The total variation (TV) model is attractive in that it is able to preserve sharp attributes in imag...
The research reported in this dissertation addresses the reconstruction of signals and images from l...
International audienceThis work focuses on several optimization problems involved in recovery of spa...