Deep-Learning Multiscale Digital Holographic Intensity and Phase Reconstruction

Abstract

Addressing the issue of the simultaneous reconstruction of intensity and phase information in multiscale digital holography, an improved deep-learning model, Mimo-Net, is proposed. For holograms with uneven distribution of useful information, local feature extraction is performed to generate holograms of different scales, branch input training is used to realize multiscale feature learning, and feature information of different receptive fields is obtained. The up-sampling path outputs multiscale intensity and phase information simultaneously through dual channels. The experimental results show that compared to Y-Net, which is a network capable of reconstructing intensity and phase information simultaneously, Mimo-Net can perform intensity and phase reconstruction simultaneously on three different scales of holograms with only one training, improving reconstruction efficiency. The peak signal-to-noise ratio and structural similarity of the Mimo-Net reconstruction for three different scales of intensity and phase information are higher than those of the Y-Net reconstruction, improving the reconstruction performance.