  In this paper, we introduce a novel unsupervised video denoising deep
learning approach that can help to mitigate data scarcity issues and shows
robustness against different noise patterns, enhancing its broad applicability.
Our method comprises three modules: a Feature generator creating features maps,
a Denoise-Net generating denoised but slightly blurry reference frames, and a
Refine-Net re-introducing high-frequency details. By leveraging the
coordinate-based network, we can greatly simplify the network structure while
preserving high-frequency details in the denoised video frames. Extensive
experiments on both simulated and real-captured demonstrate that our method can
effectively denoise real-world calcium imaging video sequences without prior
knowledge of noise models and data augmentation during training.
