17 citations · 30 across the 10 of their papers we have counts for
10 papers
Whiteness-based parameter selection for Poisson data in variational image processing
Francesca Bevilacqua, Alessandro Lanza, Monica Pragliola +1
We propose a novel automatic parameter selection strategy for variational imaging problems under Poisson noise corruption. The selection of a suitable regularization parameter, who…
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…
Adaptive parameter selection for weighted-TV image reconstruction problems
Luca Calatroni, Alessandro Lanza, Monica Pragliola +1
We propose an efficient estimation technique for the automatic selection of locally-adaptive Total Variation regularisation parameters based on an hybrid strategy which combines a…
A flexible space-variant anisotropic regularisation for image restoration with automated parameter selection
Luca Calatroni, Alessandro Lanza, Monica Pragliola +1
We propose a new space-variant anisotropic regularisation term for variational image restoration, based on the statistical assumption that the gradients of the target image distrib…
Vessel Segmentation in Medical Imaging Using a Tight-Frame Based Algorithm
Xiaohao Cai, Raymond Chan, Serena Morigi +1
Tight-frame, a generalization of orthogonal wavelets, has been used successfully in various problems in image processing, including inpainting, impulse noise removal, super-resolut…