Structured pursuits for geometric super-resolution

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 1¹ 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.