Open AccessSingle-pixel compressive optical image hiding based on conditional generative adversarial network
Author(s) -
Jiaosheng Li,
Yuhui Li,
Li Ju,
Qinnan Zhang,
Jun Li
Publication year - 2020
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.399065
Subject(s) - compressed sensing , computer science , artificial intelligence , pixel , generative adversarial network , image quality , iterative reconstruction , computer vision , sampling (signal processing) , image (mathematics) , optics , algorithm , detector , pattern recognition (psychology) , telecommunications , physics
We present a deep learning (DL) framework based on a conditional generative adversarial network (CGAN) to perform compressive optical image hiding (COIH) with a single-pixel detector. An end-to-end compressive sensing generative adversarial network (eCSGAN) is developed, achieving the approximate equivalent model of an inverse system of a nonlinear COIH model, to reconstruct two-dimensional secret images directly from real acquired one-dimensional compressive sampling signals without the need of any security keys of the COIH system such as the sequence of illumination patterns, the host image, etc. Moreover, detailed comparisons between the image reconstructed using eCSGAN and compressive sensing (CS) shows that the proposed method can remarkably increase the quality in image reconstruction with a lower sampling rate. The feasibility and security of the proposed method are demonstrated by the numerical simulations and optical experiment results.