AUTHOR=Luo Liyong , Xu Yuanxu , Pan Junxia , Wang Meng , Guan Jiangheng , Liang Shanshan , Li Yurong , Jia Hongbo , Chen Xiaowei , Li Xingyi , Zhang Chunqing , Liao Xiang 

TITLE=Restoration of Two-Photon Ca2+ Imaging Data Through Model Blind Spatiotemporal Filtering

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 15 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.630250

DOI=10.3389/fnins.2021.630250

ISSN=1662-453X

ABSTRACT=Two-photon Ca2+ imaging is a leading technique for recording neuronal activities in vivo with cellular or subcellular resolution. However, during experiments, the images often suffer corruption due to complex noises. Therefore, the analysis of Ca2+ imaging data requires preprocessing steps, such as denoising, to extract biologically relevant information. We present an approach that facilitates imaging data restoration through image denoising performed by a neural network combining spatio-temporal filtering and model blind learning. Tests with synthetic and real two-photon Ca2+ imaging datasets demonstrate that the proposed approach enables efficient restoration of the imaging data. In addition, we demonstrate that the proposed approach outperforms the current state-of-the-art methods by evaluating the qualities of the denoising performance of the models quantitatively. Therefore, our method provides an invaluable tool for denoising two-photon Ca2+ imaging data by model blind spatio-temporal processing.