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Signal recovery from nonlinear measurements involves solving an iterativeoptimization problem. In this paper, we present a framework to optimize thesensing parameters to improve the quality of the signal recovered by the giveniterative method. In particular, we learn illumination patterns to recoversignals from coded diffraction patterns using a fixed-cost alternatingminimization-based phase retrieval method. Coded diffraction phase retrieval isa physically realistic system in which the signal is first modulated by asequence of codes before the sensor records its Fourier amplitude. We representthe phase retrieval method as an unrolled network with a fixed number of layersand minimize the recovery error by optimizing over the measurement parameters.Since the number of iterations/layers are fixed, the recovery incurs a fixedcost. We present extensive simulation results on a variety of datasets underdifferent conditions and a comparison with existing methods. Our resultsdemonstrate that the proposed method provides near-perfect reconstruction usingpatterns learned with a small number of training images. Our proposed methodprovides significant improvements over existing methods both in terms ofaccuracy and speed.

Learning Illumination Patterns for Coded Diffraction Phase Retrieval