Single photon compressive imaging with enhanced quality using an untrained neural network

Traditional single photon compressive imaging has poor imaging quality. Although the method of deep learning can alleviate the problem, the harsh training sets have become a problem. In this paper, an untrained neural network is used to address this problem. A whole imaging system was established, and simulation studies based on the Monte Carlo method have been undertaken. The results show that the proposed method has improved the image quality and solved the troublesome training sets problem while ensuring imaging speed. At the same time, the discussion of input pictures, imaging type, and anti-noise capability provide a way to prove CNN’s tendency to natural images. It is also found that the network changes the sensitivity of the system to the photon numbers. The research work will provide some basis for subsequent study on single compressive photon imaging and untrained neural networks.