Single Pixel Imaging Based on Generative Adversarial Network Optimized With Multiple Prior Information

IEEE Photonics Journal

Qiurong Yan¹  Jian Lin¹  Yongjian Zheng¹  Zhen Wei¹  Yilin Cai¹  Shida Sun¹ 

[1] School of Information Engineering, Nanchang University, Nanchang, China;

关键词: Compressed sensing (CS);    single pixel imaging (SPI) system;    photon counting;    generative adversarial networks (GAN);    deep learning;    multiple prior information;   

DOI  :  10.1109/JPHOT.2022.3184947

来源: DOAJ

【 摘 要 】

Reconstructing high-quality images at low measurement rate is one of the research objectives for single-pixel imaging (SPI). Deep learning based compressed reconstruction methods have been shown to avoid the huge iterative computation of traditional methods, while achieving better reconstruction results. Benefiting from improved modeling capabilities under the constant game of generation and identification, Generative Adversarial Networks (GANs) has achieved great success in image generation and reconstruction. In this paper, we proposed a GAN-based compression reconstruction network, MPIGAN. In order to obtain multiple prior information from the dataset and thus improving the accuracy of the model, multiple Autoencoders are trained as regularization terms to be added to the loss function of the generative network, and then adversarial training is performed with a multi-label classification network. Experimental results show that our scheme can significantly improve reconstruction quality at a very low measurement rate, and reconstruction results are better than the existing network.

【 授权许可】

Unknown