Optimization of computer-generated holograms by an artificial neural network

Several computer-generated hologram (CGH) methods, such as the direct binary search, simulated annealing and genetic algorithm, have been proposed or used in order to decrease the quantum noise and reconstruction noise or to optimize the CGH. Since these methods are iterative approaches, they require long computation time to generate a CGH. In this paper, we propose a new method based on an artificial neural network (ANN) to reduce the high computation cost. In this scheme, we first use a couple of known optimized CGHs, which may be obtained by the traditional optimization methods, as teaching signals to train the ANN. With the trained ANN, we can easily and quickly obtain an optimized CGH without the optimization process for other input images.