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

Abstract

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.