.png){kind=icon}
返回
Single-pixel compressive optical image hiding based on conditional generative adversarial network
.png){kind=icon}
OA
AI
DOI：10.1364/OE.399065.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We present a deep learning (DL) framework based on a conditional generative adversarial network (CGAN) to perform compressive optical image hiding (COIN) 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. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Keyword:
ENCRYPTION
WATERMARKING
期刊
IF:
3.3
论文数:
6.1W
被引数:
14.3W
机构
引用论文
分享收藏eHoloNet: a learning-based end-to-end approach for in-line digital holographic reconstruction.png)
OPTICS EXPRESS
OAAI
OPTICS EXPRESSIF3.3
OAAI分享收藏Machine-learning attacks on interference-based optical encryption: experimental demonstrationOPTICS EXPRESSOAAI
OPTICS EXPRESSIF3.3
OAAI分享收藏
查看更多内容