Abstract
Super-resolution image zooming is possible when the image has some geometric regularity. We introduce a general class of non-linear inverse estimators, which combines linear estimators with mixing weights in a frame providing a sparse representation. Mixing weights are computed with a block decomposition, which minimizes a Tikhonov energy penalized by an l1 norm of the mixing weights. A fast orthogonal matching pursuit algorithm computes the mixing weights. Adaptive directional image interpolations are calculated with mixing weights in a wavelet frame.
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