Dataset enhancement training of diffraction model-driven neural networks and extension to generate multi-depth computer-generated holograms

Junchang Peng; Huadong Zheng; Zhen Wang; Yingjie Yu

doi:10.1117/12.3023148

15 February 2024 Dataset enhancement training of diffraction model-driven neural networks and extension to generate multi-depth computer-generated holograms

Junchang Peng, Huadong Zheng, Zhen Wang, Yingjie Yu

Author Affiliations +

Proceedings Volume 13069, International Conference on Optical and Photonic Engineering (icOPEN 2023); 1306902 (2024) https://doi.org/10.1117/12.3023148
Event: International Conference on Optical and Photonic Engineering (icOPEN 2023), 2023, Singapore, Singapore

Abstract

Learning-based computer-generated holography (CGH) has great potential for real-time, multi-depth holographic displays. However, most existing algorithms only use the amplitude of the target image as a dataset to simplify the algorithmic process. This does not adequately consider the incorporation of angular spectrum (ASM) method into neural networks that can compute multiplanar attributes. Here, we propose a multi-depth diffraction model-driven neural network (MD-Holo). MD-Holo utilizes the weights of the pre-trained ResNet34 as initialization in the encoder stage of the complex amplitude generating network to extract more general features. Motion blur, Gaussian filtering, lens blur and low-pass filtered images are added to accommodate a wider range of images. Compared to the super-resolution DIV2K dataset alone, the use of the enhanced dataset allows both the generation of high-fidelity super-resolution images and the generalization of a wider variety of images. Simulations and optical experiments show that MD-Holo can reconstruct multi-depth images with high quality and fewer artifacts.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Junchang Peng, Huadong Zheng, Zhen Wang, and Yingjie Yu "Dataset enhancement training of diffraction model-driven neural networks and extension to generate multi-depth computer-generated holograms", Proc. SPIE 13069, International Conference on Optical and Photonic Engineering (icOPEN 2023), 1306902 (15 February 2024); https://doi.org/10.1117/12.3023148

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KEYWORDS

Education and training

3D image reconstruction

Image processing

Computer generated holography

Image restoration

Neural networks

Image filtering

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