Interpretable Poisson Optimization-Inspired Deep Network for Single-Photon Counting Image Denoising

Single-photon counting (SPC) imaging is a versatile approach for detecting targets under extremely low-light situations. To increase the quality of SPC images degraded by noise, traditional optimization-based methods seek priors from handcrafted features, which are inadequate to handle different kinds of targets in natural scenes, leading to poor denoising performance. The difficulty of optimal parameter design for good balance between denoising performance and spatial clarity is another obstacle. To address these issues, we develop a novel interpretable Poisson optimization-inspired deep network for SPC image denoising. First, we build a learnable prior that regularizes the Poisson optimization problem for SPC imaging to enhance the denoising performance. We construct a deep network by unfolding the iterative shrinkage-thresholding algorithm to solve the Poisson optimization problem. Therefore, all modules in the network have strong interpretability, enabling good generalization capability in real situations. Second, all parameters are optimized in a data-driven manner in the network. Finally, we conduct both simulated and real experiments to test the effectiveness of the proposed method. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods from aspects of visual effect and quantitative analysis.