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Fourier Ptychography is a recently proposed imaging technique that yieldshigh-resolution images by computationally transcending the diffraction blur ofan optical system. At the crux of this method is the phase retrieval algorithm,which is used for computationally stitching together low-resolution imagestaken under varying illumination angles of a coherent light source. However,the traditional iterative phase retrieval technique relies heavily on theinitialization and also need a good amount of overlap in the Fourier domain forthe successively captured low-resolution images, thus increasing theacquisition time and data. We show that an auto-encoder based architecture canbe adaptively trained for phase retrieval under both low overlap, wheretraditional techniques completely fail, and at higher levels of overlap. Forthe low overlap case we show that a supervised deep learning technique using anautoencoder generator is a good choice for solving the Fourier ptychographyproblem. And for the high overlap case, we show that optimizing the generatorfor reducing the forward model error is an appropriate choice. Usingsimulations for the challenging case of uncorrelated phase and amplitude, weshow that our method outperforms many of the previously proposed Fourierptychography phase retrieval techniques.

Phase retrieval for Fourier Ptychography under varying amount of measurements