Super-Resolution With Sparse Mixing Estimators

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Super-Resolution With Sparse Mixing Estimators
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.
Stéphane Mallat, Guoshen Yu
Added 22 May 2011
Updated 22 May 2011
Type Journal
Year 2010
Where TIP
Authors Stéphane Mallat, Guoshen Yu
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