High speed phase recovery from through-focus intensity images remains challenging. The limitation lies in the accumulated exposure time of intensity measurement. In this work, we propose to recover phase from the measurement of an event camera. Event cameras asynchronously measure intensity changes at microsecond resolution. It enables capturing the through-focus intensity changes with high speed. We build the forward model of the imaging process, which incorporates the temporal noise of event camera. We develop nonlinear optimization algorithms to solve phase from measured events. We validate the algorithms with simulations and test the effects of sensitivity parameters and temporal noise.
Phase retrieval methods have been proposed to recover phase from through-focus intensity images measured on a microscope. It requires measuring the defocus distances of intensity images, usually by a z-axis translation stage. However, it complicates the experiment, adds expense, and suffers from error in the z-stage measurement. In this paper, we propose an algorithm to recover phase from only through-focus intensity images without measuring extra parameters such as defocus, wavelength and pixel size. We formulate the phase retrieval as a least-square error optimization problem and propose an alternating update scheme to solve the nonconvex optimization problem. We validate the algorithm with simulation and experiment.
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