Single photon counting compressive imaging using a generative model optimized via sampling and transfer learning.

Single photon counting compressive imaging, a combination of single-pixel-imaging and single-photon-counting technology, is provided with low cost and ultra-high sensitivity. However, it requires a long imaging time when applying traditional compressed sensing (CS) reconstruction algorithms. A deep-learning-based compressed reconstruction network refrains iterative computation while achieving efficient reconstruction.

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 ultrahigh-sensitivity photon-counting imaging. However, its applications in high-resolution 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.

A binary sampling Res2net reconstruction network for single-pixel imaging.

The traditional algorithm for compressive reconstruction has high computational complexity. In order to reduce the reconstruction time of compressive sensing, deep learning networks have proven to be an effective solution. In this paper, we have developed a single-pixel imaging system based on deep learning and designed the binary sampling Res2Net reconstruction network (Bsr2-Net) model suitable for binary matrix sampling.

Large-area single photon compressive imaging based on multiple micro-mirrors combination imaging method.

We demonstrate a single photon compressive imaging system with the image plane up to the entire digital micro-mirror device (DMD) work area. A parallel light source is designed to reduce the influence of light scattering on imaging resolution and a photon counting photomultiplier tube (PMT) with a large photosensitive area is used to effectively collect light reflected from the full screen of DMD. A control and counting circuit, based on Field-Programmable Gate Array (FPGA), is developed to load binary random matrix into the DMD controller for each measurement, and to count single-photon pulse output from PMT simultaneously.