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In this paper, we apply Bayesian blind source separation (BSS) from noisy convolutive mixtures to jointly separate and restore source images degraded through unknown blur operators, and then linearly mixed. We found that this problem arises in several image processing applications, among which there are some interesting instances of degraded document(More)
We show that computing (and even approximating) MAXIMUM CLIQUE and MINIMUM GRAPH COLORING for circulant graphs is essentially as hard as in the general case. In contrast, we show that, under additional constraints, e.g., prime order and/or sparseness, GRAPH ISOMORPHISM and MINIMUM GRAPH COLORING become easier in the circulant case, and we take advantage of(More)
In this paper 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(More)
The problem of image restoration from blur and noise is studied. A solution of the problem is understood as the minimum of an energy function composed by two terms. The first is the data fidelity term, while the latter is related to the smoothness constraints. The discontinuities of the ideal image are unknown and must be estimated. In particular, the(More)