We propose a robust variational model for the restoration of images corrupted by blur and the general class of additive white noises. The solution of the non-trivial optimization problem, due to the non-smooth non-convex proposed model, is efficiently obtained by an Alternating Directions Method of Multipliers (ADMM), which in particular reduces the solution to a sequence of convex optimization sub-problems. Numerical results show the potentiality of the proposed model for restoring blurred images corrupted by several kinds of additive white noises.

A Unified Framework for the Restoration of Images Corrupted by Additive White Noise

LANZA, ALESSANDRO;MORIGI, SERENA;SGALLARI, FIORELLA
2017

Abstract

We propose a robust variational model for the restoration of images corrupted by blur and the general class of additive white noises. The solution of the non-trivial optimization problem, due to the non-smooth non-convex proposed model, is efficiently obtained by an Alternating Directions Method of Multipliers (ADMM), which in particular reduces the solution to a sequence of convex optimization sub-problems. Numerical results show the potentiality of the proposed model for restoring blurred images corrupted by several kinds of additive white noises.
2017
Scale Space and Variational Methods in Computer Vision
498
510
Lanza, Alessandro; Sciacchitano, Federica; Morigi, Serena; Sgallari, Fiorella
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/590255
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