Abstract: The high sensitivity of single-pixel single-photon counting imaging makes it extremely advantageous in low-light detection, but the quality of reconstructed images will still degrade with the weakening of light flux. Therefore, this paper designed a single-pixel single-photon counting imaging method based on deep learning denoising to improve the signal-to-noise ratio of reconstructed images under low-light environment.Firstly, a single pixel single photon counting imaging system is established. Then, compressed sensing algorithm is used to reconstruct the image. Finally, 3D block matching algorithm and deep learning algorithm are used to denoise the reconstructed image, and the denoising effect is compared.both the denoising effect of the deep learning-based DRUnet algorithm and the 3D block matching algorithm could improve the signal-to-noise ratio of the image.The signal-to-noise ratio of the image obtained by the single pixel single-photon couting imaging method based on deep learning image denoising is increased by 12.97dB, which improves the image signal-to-noise ratio in the low-light environment, and has a higher signal-to-noise ratio than that obtained by the three-dimensional block matching algorithm.Therefore, this method provides a new idea for improving the quality of the image reconstructed by the single-pixel single-photon imaging system in the low-light environment.

Key words: Single pixel imaging, Photon counting imaging, Image denoising, Deep learning, Low light detection