Phase retrieval of sparse signals from Fourier Transform magnitude using non-negative matrix factorization
2013 IEEE Global Conference on Signal and Information Processing
1113 - 1116
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Signal and image reconstruction from Fourier Transform magnitude is a difficult inverse problem. Fourier transform magnitude can be measured in many practical applications, but the phase may not be measured. Since the autocorrelation of an image or a signal can be expressed as convolution of x(n) with x(-n), it is possible to formulate the inverse problem as a non-negative matrix factorization problem. In this paper, we propose a new algorithm based on the sparse non-negative matrix factorization (NNMF) to estimate the phase of a signal or an image in an iterative manner. Experimental reconstruction results are presented. © 2013 IEEE.
KeywordsFourier transform magnitudes
Nonnegative matrix factorization
Sparse non-negative matrix factorizations