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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 a novel and versatile group sparsity prior for denoising and to regularize inverse problems. The sparsity is enforced through arbitrary block-localization operators , such as for instance smooth localized partition functions. The resulting blocks can have an arbitrary overlap, which is important to reduce visual artifacts thanks to the(More)