Study of image reconstruction efficiency in single-pixel imaging method using generative adversarial networks

Single-pixel imaging is a promising image acquisition method that provides an alternative to traditional imaging methods using multi-pixel matrices. However, algorithmic image reconstruction from measurements of a single-pixel camera is a non-trivial computational task that can be solved by machine learning methods. In this work, we investigate the possibility of image reconstruction in the single-pixel imaging method using generative adversarial neural networks. Using computer simulation of a single-pixel camera, we study the efficiency of image reconstruction using two generative network architectures – a deep convolutional generative adversarial network and a generative least squares adversarial network. We find that the generative least squares adversarial network demonstrates a better image reconstruction quality compared to the deep convolutional generative adversarial network. However, when taking into account optical distortions, the deep convolutional adversarial network is more stable in learning to a higher quality compared to the generative least squares adversarial network. The results obtained in this work may serve as a basis for the development of software for the practical application of a single-pixel camera.