We present a Graduated Non-Convexity (GNC) algorithm for reconstructing images. We assume that the data images are blurred and corrupted by white Gaussian noise. Geometric features of discontinuities are introduced in the model and the problem is formulated as the minimization of a non-convex function. We give a convex approximation of such a function and a family of approximating functions. Moreover, to analyze the convex approximation, we prove an alternative duality theorem to implicitly treat discontinuities of images.
A GNC Algorithm for Deblurring Images with Interacting Discontinuities
BOCCUTO, Antonio;GERACE, Ivan;PUCCI, Patrizia
2002
Abstract
We present a Graduated Non-Convexity (GNC) algorithm for reconstructing images. We assume that the data images are blurred and corrupted by white Gaussian noise. Geometric features of discontinuities are introduced in the model and the problem is formulated as the minimization of a non-convex function. We give a convex approximation of such a function and a family of approximating functions. Moreover, to analyze the convex approximation, we prove an alternative duality theorem to implicitly treat discontinuities of images.File in questo prodotto:
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