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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 article proposes a new framework to regularize linear inverse problems using a total variation prior on an adapted non-local graph. The non-local graph is optimized to match the structures of the image to recover. This allows a better reconstruction of geometric edges and textures present in natural images. A fast algorithm computes iteratively both(More)