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—This paper studies the properties of ℓ 1-analysis regularization for the resolution of linear inverse problems. Most previous works consider sparse synthesis priors where the sparsity is measured as the ℓ 1 norm of the coefficients that synthesize the signal in a given dictionary. In contrast, the more general analysis regularization minimizes the ℓ 1 norm(More)
Figure 1. Different steps in mesh parameterization. Abstract In this paper, we present a method for remeshing trian-gulated manifolds by using geodesic path calculations and distance maps. Our work builds on the Fast Marching algorithm , which has been extended to arbitrary meshes by Sethian and Kimmel in [17]. First, a set of points that are evenly spaced(More)
This paper introduces the generalized forward-backward splitting algorithm for minimizing convex functions of the form F + ∑ n i=1 G i , where F has a Lipschitz-continuous gradient and the G i 's are simple in the sense that their Moreau proximity operators are easy to compute. While the forward-backward algorithm cannot deal with more than n = 1 non-smooth(More)
This article proposes a new framework to regularize linear inverse problems using the total variation on non-local graphs. This non-local graph allows to adapt the penalization to the geometry of the underlying function to recover. A fast algorithm computes iteratively both the solution of the regularization process and the non-local graph adapted to this(More)