CNES research project Image restoration The problem of recovering an image that has blurred and corrupted with an additive noise is a classical problem in image processing. There is no really agreement on an optimal solution. Among the methods that have been proposed to solve this problem, two of the most successful are that of Total Variation (TV) image restoration proposed by L. Rudin, S Osher and E. Fatimi in their seminal work and the thresholding of wavelet coefficient decomposition (cf. D. Donoho, Y. Meyer…). These two kinds of methods are quasi equivalent up to a different handling. TV restoration requires the choice from all the TV images that is smallest: TV measures the discontinuity weighted by its jump. This hypothesis is not reasonable to recovering texture areas. VT restorated images are corrupted with "pixellised" phenomenon on homogeneous areas. It is not observed with thresholding method but the impulse response of wavelet is visible on the images. In order to manage this thresholding and to take into account edges, frame geometric approach has been proposed. S. Mallat (bandelet decomposition) proposes an adaptive method and E. Candes a systematic one (curvelet decomposition). The project will consist in tutorial on satellite images. Results will be analyzed and we will look for theoretical explanation (image regularity, TV or Besov image representation space, optimality theorem etc.). The aim is to gather researchers and engineers who are interested in all restoration image aspects. Participants: Andrès Almansa, Sylvain Durand, Yves Meyer, Jean-Michel Morel, Mila NiKolova, Bernard Rougé.