Non-Iterative Superresolution Phase Retrieval of Sparse Images without Support Constraints
نویسنده
چکیده
We propose a new non-iterative algorithm for phase retrieval of a sparse image from lowwavenumber values of its Fourier transform magnitude. No image support constraint is needed. The algorithm uses the sparsity of the image autocorrelation to reconstruct it exactly from low-wavenumber Fourier magnitude data (superresolution) using a variation of MUSIC. The sparsity of the image is then used to reconstruct it recursively from its autocorrelation. Applications include X-ray crystallography and astronomy. Three numerical examples illustrate the algorithm. Keywords— Superresolution, phase retrieval Phone: 734-763-9810. Fax: 734-763-1503. Email: [email protected]. EDICS: 2-REST.
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