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Super-Resolution With Sparse Mixing Estimators
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DOI：10.1109/TIP.2010.2049927.png)
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Abstract
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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 1(1) 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.
Keywords:
Block matching pursuit
interpolation
inverse problem
mixing estimator
structured sparsity
super-resolution
Tikhonov regularization
wavelet
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