Nov 8, 2009 · We adopt a convex optimization framework where the criterion to be minimized is split in the sum of more than two terms. For spatial domain ...

Mar 17, 2011 · To solve this problem, we adopt a convex optimization framework where the criterion to be minimized is split in the sum of more than two terms.

To solve this problem, we adopt a convex optimization framework where the criterion to be minimized is split in the sum of more than two terms. For spatial ...

Mar 8, 2011 · Regularization approaches have demonstrated their effectiveness for solving ill-posed prob- lems. However, in the context of variational ...

Abstract—Regularization approaches have demon- strated their effectiveness for solving ill-posed problems. However, in the context of variational ...

Abstract: Regularization approaches have demonstrated their effectiveness for solving ill-posed problems. However, in the context of variational restoration ...

[1] as a regularization approach capable of reducing noise, while preserving image edges. It is often interpreted as a sparsity-promoting `1-penalty on the.

Nov 12, 2010 · Abstract—To improve the estimation at the voxel level in dynamic Positron Emission Tomography (PET) imaging, we.

A proximal algorithm is an algorithm for solving a convex optimization problem that uses the proximal operators of the objective terms. For example, the ...