Abstract

Sparse representation of data has grown rapidly in signal processing. Herein we represent atmospheric turbulence point-spread-functions by training optimal redundant dictionaries from atmospheric turbulence data. Implications for blind-deconvolution of turbulent images are discussed.

© 2014 OSA

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