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.

• Type: Journal
• Year: 2010
• Where: TIP
• Authors: Stéphane Mallat, Guoshen Yu
• Keywords: Adaptive Directional Image, Education, Inverse Problem Estimators, Sparse Signal Representation, TIP 2010