Ghost edge detection based on HED network

doi:10.1007/s12200-022-00036-1

Front. Optoelectron.

2022, Vol. 15

Issue (3) : 31 https://doi.org/10.1007/s12200-022-00036-1

RESEARCH ARTICLE

Ghost edge detection based on HED network

Shengmei Zhao^1,²(

), Yifang Cui¹, Xing He¹, Le Wang¹(

)

¹. Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications (NUPT), Nanjing 210003, China
². Key Lab of Broadband Wireless Communication and Sensor Network Technology (NUPT), Ministry of Education, Nanjing 210003, China

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Abstract

In this paper, we present an edge detection scheme based on ghost imaging (GI) with a holistically-nested neural network. The so-called holistically-nested edge detection (HED) network is adopted to combine the fully convolutional neural network (CNN) with deep supervision to learn image edges effectively. Simulated data are used to train the HED network, and the unknown object’s edge information is reconstructed from the experimental data. The experiment results show that, when the compression ratio (CR) is 12.5%, this scheme can obtain a high-quality edge information with a sub-Nyquist sampling ratio and has a better performance than those using speckle-shifting GI (SSGI), compressed ghost edge imaging (CGEI) and subpixel-shifted GI (SPSGI). Indeed, the proposed scheme can have a good signal-to-noise ratio performance even if the sub-Nyquist sampling ratio is greater than 5.45%. Since the HED network is trained by numerical simulations before the experiment, this proposed method provides a promising way for achieving edge detection with small measurement times and low time cost.

Keywords Edge detection Ghost imaging (GI) Holistically-nested neural network Compression ratio (CR) Signal-to-noise ratio (SNR)

Corresponding Author(s): Shengmei Zhao,Le Wang

About author: Tongcan Cui and Yizhe Hou contributed equally to this work.

Issue Date: 23 August 2022

Cite this article:

Shengmei Zhao,Yifang Cui,Xing He, et al. Ghost edge detection based on HED network[J]. Front. Optoelectron., 2022, 15(3): 31.

URL:

https://academic.hep.com.cn/foe/EN/10.1007/s12200-022-00036-1
https://academic.hep.com.cn/foe/EN/Y2022/V15/I3/31

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