5 citations · 12 across the 5 of their papers we have counts for
7 papers
ADMM-based residual whiteness principle for automatic parameter selection in super-resolution problems
Monica Pragliola, Luca Calatroni, Alessandro Lanza +1
We propose an automatic parameter selection strategy for the problem of image super-resolution for images corrupted by blur and additive white Gaussian noise with unknown standard…
On and beyond Total Variation regularisation in imaging: the role of space variance
Monica Pragliola, Luca Calatroni, Alessandro Lanza +1
Over the last 30 years a plethora of variational regularisation models for image reconstruction has been proposed and thoroughly inspected by the applied mathematics community. Amo…
Residual whiteness principle for automatic parameter selection in - image super-resolution problems
Monica Pragliola, Luca Calatroni, Alessandro Lanza +1
We propose an automatic parameter selection strategy for variational image super-resolution of blurred and down-sampled images corrupted by additive white Gaussian noise (AWGN) wit…
Space-adaptive anisotropic bivariate Laplacian regularization for image restoration
Luca Calatroni, Alessandro Lanza, Monica Pragliola +1
In this paper we present a new regularization term for variational image restoration which can be regarded as a space-variant anisotropic extension of the classical isotropic Total…
Space-variant Generalized Gaussian Regularization for Image Restoration
Alessandro Lanza, Serena Morigi, Monica Pragliola +1
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 acc…
Space-variant TV regularization for image restoration
Alessandro Lanza, Serena Morigi, Monica Pragliola +1
We propose two new variational models aimed to outperform the popular total variation (TV) model for image restoration with L and L fidelity terms. In particular, we introd…