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Super-Resolution With Sparse Mixing Estimators

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2 Author(s)
Stéphane Mallat ; CMAP, Ecole Polytechnique, Palaiseau Cedex, France ; Guoshen Yu

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 log N) algorithm, providing state-of-the-art numerical results.

Published in:

IEEE Transactions on Image Processing  (Volume:19 ,  Issue: 11 )