Supplementary Dataset For: Simultaneous Removing of Noise and Correction of Motion Warping in Neuron Calcium Imaging Using a Pipeline Structure of Self-supervised Deep Learning Models

Calcium imaging visualizes specific activity of neurons through active sensors, which makes it easy to study the neuronal behavior of animals' learning processes and cognition and helps to promote the use of animal models for neuroscience research. However, motion artifacts and background noise can affect calcium imaging, especially when watching awake animals while they are exposed to low-dose laser irradiation. This makes it impossible to fully understand how neural circuits work. As a result, imaging results are often warped and contain significant random noise. This dataset contains two simultaneously warped and noised calcium imaging stacks collected from a confocal microscopy calcium imaging system without postprocessing.