Computing 3D Phase-Type Holograms Based on Deep Learning Method

Huadong Zheng,Jianbin Hu,Xiaoxi Wang,Chaojun Zhou

doi:10.3390/photonics8070280

Abstract

Computer holography is a technology that use a mathematical model of optical holography to generate digital holograms. It has wide and promising applications in various areas, especially holographic display. However, traditional computational algorithms for generation of phase-type holograms based on iterative optimization have a built-in tradeoff between the calculating speed and accuracy, which severely limits the performance of computational holograms in advanced applications. Recently, several deep learning based computational methods for generating holograms have gained more and more attention. In this paper, a convolutional neural network for generation of multi-plane holograms and its training strategy is proposed using a multi-plane iterative angular spectrum algorithm (ASM). The well-trained network indicates an excellent ability to generate phase-only holograms for multi-plane input images and to reconstruct correct images in the corresponding depth plane. Numerical simulations and optical reconstructions show that the accuracy of this method is almost the same with traditional iterative methods but the computational time decreases dramatically. The result images show a high quality through analysis of the image performance indicators, e.g., peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and contrast ratio. Finally, the effectiveness of the proposed method is verified through experimental investigations.

Highlights

Computer-generated holograms (CGHs) are widely used in various fields [1,2], since computational holography can record and reproduce the amplitude and phase of light waves comprehensively, and has the advantages of low noise and high reproducibility [3]
For CGH generation, there is an unavoidable problem with the iterative methods because they require a trade-off between computation time and image quality caused by the iterative process [7,8,9]
For the holograms produced by the trained network, it is necessary to verify whether it has the ability to reconstruct different images at the corresponding depths like the reconstruction results of ground-truth CGHs and it is necessary to compare whether the two results are similar, so as to evaluate whether the network has effectively learned the light propagation process of the iterative ASM

Summary

Introduction

Computer-generated holograms (CGHs) are widely used in various fields [1,2], since computational holography can record and reproduce the amplitude and phase of light waves comprehensively, and has the advantages of low noise and high reproducibility [3]. It can generate holograms of virtual objects, compared with traditional optical holography. Non-iterative complex amplitude encoding methods have been proposed to achieve fast CGH calculation with guaranteed imaging quality, such as the error diffusion method [10] and the double phase encoding method [11,12]

Results

Discussion

Conclusion