Restoration of Two-Photon Ca2+ Imaging Data Through Model Blind Spatiotemporal Filtering - Luo, Liyong; Xu, Yuanxu; Pan, Junxia; Wang, Meng; Guan, Jiangheng; Liang, Shanshan; Li, Yurong; Jia, Hongbo; Chen, Xiaowei; Li, Xingyi; Zhang, Chunqing; Liao, Xiang (Frontiers in Neuroscience, 2021)
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.