We present DeepVIDv2, a resolution-improved self-supervised voltage imaging denoising approach that achieves higher spatial resolution while preserving fast neuronal dynamics. While existing methods enhance signal-to-noise ratio (SNR), they compromise spatial resolution and result in blurry outputs. By disentangling spatial and temporal performance into two parameters, DeepVIDv2 overcomes the tradeoff faced by its predecessor. This advancement enables more effective analysis of high-speed, large-population voltage imaging data.
High-speed low-light two-photon voltage imaging is an emerging tool to simultaneously monitor neuronal activity from a large number of neurons. However, shot noise dominates pixel-wise measurements and the neuronal signals are difficult to be identified in the single-frame raw measurement. We developed a self-supervised deep learning framework for voltage imaging denoising, DeepVID, without the need for any high-SNR measurements. DeepVID infers the underlying fluorescence signal based on independent temporal and spatial statistics of the measurement that is attributable to shot noise. DeepVID achieved a 15-fold improvement in SNR when comparing denoised and raw image data.
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