Deep-learning-based single photon counting compressive imaging via jointly trained subpixel convolution sampling

The combination of single pixel imaging and single photon counting technology can achieve ultra-high sensitivity photon counting imaging. However, its applications in highresolution and real-time scenarios are limited by the long sampling and reconstruction time. Deep learning based compressive sensing provides an effective solution due to its ability to achieve fast and high-quality reconstruction. This paper proposes a sampling and reconstruction integrated neural network (HRSC-Net) for single photon counting compressive imaging. To effectively remove the blocking artifact, a sub-pixel convolutional layer is jointly trained with deep reconstruction network to imitate compressed sampling. By modifying the forward and backward propagation of the network, the first layer is trained into a binary matrix which can be applied to the imaging system. An improved deep reconstruction network based on the traditional Inception network is proposed and the experimental results show that its reconstruction quality is better than existing deep-learning-based compressive sensing reconstruction algorithms.