Robust single-photon counting imaging with spatially correlated and total variation constraints

Single-photon counting imaging is a novel computational imaging technique that counts every photon collected by reflective light; it has target detection capability under extremely low-light conditions and thus has elicited increasing research interest. However, a low single-photon counting number and considerable noise will significantly affect image quality under low-light conditions. To improve the quality of single-photon counting image efficiently, we propose a robust single-photon counting imaging method with spatially correlated and total variation (TV) constraints. A robust Poisson negative log-likelihood function is introduced as a data fidelity term, which is robust to some spatial points that have extremely large background count in real situations. The TV regularization constraint is adopted to reduce noise. Considering that the reflectivity of several spatially correlated points may be similar, we suggest adding another constraint based on the counting information from these points rather than a single point for estimating reflectivity in each pixel. This approach will be helpful in reducing truncation errors. The proposed imaging model is formulated on the basis of the aforementioned factors. The alternative direction multiplier method is used to solve the optimization problem. The superiority of the proposed method over state-of-the-art techniques is verified on simulated and real captured experimental datasets under different conditions.