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An Adaptive Inverse Scale Space Method for Compressed Sensing

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In this talk a novel adaptive approach for solving -minimization problems as frequently arising in compressed sensing is introduced, which is based on the recently introduced inverse scale space method. The scheme allows to efficiently compute minimizers by solving a sequence of low-dimensional nonnegative least-squares problems. Moreover, extensive comparisons between the proposed method and the related orthogonal matching pursuit algorithm are presented.

This talk is part of the Cambridge Image Analysis Seminars series.

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