title: Super-resolution with sparse mixing estimators.
abstract: We introduce a class of inverse problem estimators computed by mixing adaptively a family of linear estimators corresponding to different priors. Sparse mixing weights are calculated over blocks of coefficients in a frame providing a sparse signal representation. They minimize an l1 norm taking into account the signal regularity in each block. Adaptive directional image interpolations are computed over a wavelet frame with an O(N logN) algorithm, providing state-of-the-art numerical results.
authors.forename: Stéphane
authors.initials: S
authors.lastname: Mallat
authors.forename: Guoshen
authors.initials: G
authors.lastname: Yu