Denoising method based on deep learning used in phase retrieval of holographic storage

The single-shot iterative Fourier transform algorithm as a common non-interferometric phase retrieval algorithm is very suitable for phase-modulated holographic data storage due to its fast, simple and stable properties. It retrieves the phase in the object domain iteratively from the intensity image in the Fourier domain captured by the detector. Because of the effects by complex noises of the experimental system, there is always an intensity image degradation which increases the phase decoding bit error rate. This paper proposed a denoising method based on end-to-end convolutional neural networks by learning the relationship between the captured intensity images and the simulation results to improve image quality significantly. Then the denoised intensity image was used in the phase retrieval. The experiment results showed that the bit error rate can be reduced by 6.7 times using the denoised image, which proved the feasibility of the neural network denoising method in the phase-modulated holographic data storage system.