Open AccessPhase-only hologram generated by a convolutional neural network trained using low-frequency mixed noise
Author(s) -
Xi Wang,
Xinlei Liu,
Jing Tao,
Pei Li,
Xiaoyu Jiang,
Qiang Liu,
Xingpeng Yan
Publication year - 2022
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.466083
Subject(s) - holography , computer science , convolutional neural network , noise (video) , artificial intelligence , convolution (computer science) , pixel , optics , phase (matter) , image quality , frame (networking) , computer vision , pattern recognition (psychology) , artificial neural network , image (mathematics) , physics , telecommunications , quantum mechanics
A phase-only hologram generated through the convolution neutral network (CNN) which is trained by the low-frequency mixed noise (LFMN) is proposed. Compared with CNN based computer-generated holograms, the proposed training dataset named LFMN includes different kinds of noise images after low-frequency processing. This dataset was used to replace the real images used in the conventional hologram to train CNN in a simple and flexible approach. The results revealed that the proposed method could generate a hologram of 2160 × 3840 pixels at a speed of 0.094 s/frame on the DIV2K valid dataset, and the average peak signal-to-noise ratio of the reconstruction was approximately 29.2 dB. The results of optical experiments validated the theoretical prediction. The reconstructed images obtained using the proposed method exhibited higher quality than those obtained using the conventional methods. Furthermore, the proposed method considerably mitigated artifacts of the reconstructed images.