Imaging quality enhancement in photon-counting single-pixel imaging via an ADMM-based deep unfolding network in small animal fluorescence imaging

Abstract

Photon-counting single-pixel imaging (SPI) can image under low-light conditions with high-sensitivity detection. However, the imaging quality of these systems will degrade due to the undersampling and intrinsic photon-noise in practical applications. Here, we propose a deep unfolding network based on the Bayesian maximum a posterior (MAP) estimation and alternating direction method of multipliers (ADMM) algorithm. The reconstruction framework adopts a learnable denoiser by convolutional neural network (CNN) instead of explicit function with hand-crafted prior. Our method enhances the imaging quality compared to traditional methods and data-driven CNN under different photon-noise levels at a low sampling rate of 8%. Using our method, the sensitivity of photon-counting SPI prototype system for fluorescence imaging can reach 7.4 pmol/ml. In-vivo imaging of a mouse bearing tumor demonstrates an 8-times imaging efficiency improvement.