Phase retrieval combined with the deep learning denoising method in holographic data storage

We proposed a phase retrieval combined with the deep learning denoising method in holographic data storage. By learning the relationship between the captured intensity images and the simulation truth images, the deep learning convolutional neural network can have a good grasp of the complex noise patterns in the captured images. Therefore, we can denoise the single-shot captured image to improve image quality significantly. We used the denoised image to retrieve phase by combining single-shot iterative Fourier transform algorithm. 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. We also analyzed the tolerances of our method to show its practicability.

Automated summary

This research proposes a phase retrieval method combined with deep learning denoising in holographic data storage. By learning the relationship between captured images and simulation images, a convolutional neural network can effectively denoise and improve image quality. Experimental results demonstrate a significant reduction in bit error rate using the denoised images, proving the feasibility of the neural network denoising method in phase-modulated holographic data storage systems.