DeepVID v2: Self-Supervised Denoising with Decoupled Spatiotemporal Enhancement for Low-Photon Voltage Imaging.

Significance: Voltage imaging is a powerful tool for studying the dynamics of neuronal activities in the brain. However, voltage imaging data are fundamentally corrupted by severe Poisson noise in the low-photon regime, which hinders the accurate extraction of neuronal activities. Self-supervised deep learning denoising methods have shown great potential in addressing the challenges in low-photon voltage imaging without the need for ground truth, but usually suffer from the tradeoff between spatial and temporal performance. Aim: We present DeepVID v2, a novel self-supervised denoising framework with decoupled spatial and temporal enhancement capability to significantly augment low-photon voltage imaging. Approach: DeepVID v2 is built on our original DeepVID framework, Results: We demonstrate that DeepVID v2 is able to overcome the tradeoff between spatial and temporal performance, and achieve superior denoising capability in resolving both high-resolution spatial structures and rapid temporal neuronal activities. We further show that DeepVID v2 is able to generalize to different imaging conditions, including time-series measurements with various signal-to-noise ratios (SNRs) and in extreme low-photon conditions. Conclusions: Our results underscore DeepVID v2 as a promising tool for enhancing voltage imaging. This framework has the potential to generalize to other low-photon imaging modalities and greatly facilitate the study of neuronal activities in the brain.