Using Black-Box Compression Algorithms for Phase Retrieval

Compressive phase retrieval refers to the problem of recovering a structured n-dimensional complex-valued vector from its phase-less under-determined linear measurements. The non-linearity of the measurement process makes designing theoretically-analyzable efficient phase retrieval algorithms challenging. As a result, to a great extent, existing recovery algorithms only take advantage of simple structures such as sparsity and its convex generalizations. The goal of this article is to move beyond simple models through employing compression codes. Such codes are typically developed to take advantage of complex signal models to represent the signals as efficiently as possible. In this work, it is shown how an existing compression code can be treated as a black box and integrated into an efficient solution for phase retrieval. First, COmpressive PhasE Retrieval (COPER) optimization, a computationally-intensive compression-based phase retrieval method, is proposed. COPER provides a theoretical framework for studying compression-based phase retrieval. The number of measurements required by COPER is connected to kappa, the alpha-dimension (closely related to the ratedistortion dimension) of a given family of compression codes. To finds the solution of COPER, an efficient iterative algorithm called gradient descent for COPER (GD-COPER) is proposed. It is proven that under some mild conditions on the initialization and the compression code, if the number of measurements is larger than C kappa(2) log(2) n, where C is a constant, GD-COPER obtains an accurate estimate of the input vector in polynomial time. In the simulation results, JPEG2000 is integrated in GD-COPER to confirm the state-of-the-art performance of the resulting algorithm on real-world images.