Efficient InSAR phase noise reduction via total variation regularization

Directional Total Generalized Variation Regularization for Impulse Noise Removal

Total Variation Regularization in

We propose computational algorithms for incorporating total varia-tional (TV) regularization in positron emission tomography (PET). The motivation for using TV is that it has been shown to suppress noise effectively while capturing sharp edges without oscillations. This feature makes it particularly attractive for those applications of PET where the objective is to identify the shape of objects...

Speckle Reduction Variational Model with Total Variation Regularization

In coherent imaging systems, such as synthetic aperture radar (SAR), the observed images are contaminated by the speckle noise (multiplicative noise). Due to the edge preserving feature of the total variation (TV), variational models with TV regularization have attracted much interest in removing the speckle noise. However, the fidelity term of the variational model, based on maximum a posterio...

Discrete Total Variation Flows without Regularization

We propose and analyze an algorithm for the solution of the L2-subgradient flow of the total variation functional. The algorithm involves no regularization, thus the numerical solution preserves the main features that motivate practitioners to consider this type of energy. We propose an iterative scheme for the solution of the arising problems, show that the iterations converge, and develop a s...

Total Variation Regularization in Electrical Impedance Tomography

This paper presents an evaluation of the use of Primal Dual Methods for efficiently regularizing the electric impedance tomography (EIT) problem with the Total Variation (TV) functional. The Total Variation functional is assuming an important role in the regularization of inverse problems thanks to its ability to preserve discontinuities in reconstructed profiles. This property is desirable in ...