Fig. 1 |. Training DeepInterpolation networks for denoising two-photon Ca²⁺ imaging.

(A) Schematic of an encoder-decoder deep network with skip connections for denoising two-photon imaging data. The encoder-decoder network utilized Npre and Npost frames, acquired at 30 Hz, before and after the target frame (labelled in green) to predict the central target frame (which was omitted from the input). Scale bar is 50 μm (B) Left: Validation loss shown as a function of unique samples for different combinations of (Npre, Npost) values during training for training a single network for the Ai93 reporter line (Methods). Y axis is the mean absolute difference between a predicted frame and a noisy sample across 2500 samples. Individual data samples were z-scored using a single estimate of mean and standard deviation per movie. Validation loss was therefore measured in normalized fluorescence units. Dashed vertical line indicates early stopping (due to the extreme computational demands during training) during evaluation of the parameter sets. Right: denoising performance of this model compared to a single raw frame (top) on a single representative experiment. Scale bar is 100 μm. Each red inset is 100 μm. (C) Six representative example traces extracted from a held-out denoised movie before (black) and after (red) denoising with DeepInterpolation. The top three traces are extracted from a somatic ROI, while the bottom three traces are extracted from a single pixel. (D) Distribution of SNR (mean over standard deviation, see Methods) for 10,000 pixels (randomly selected across N=19 denoised held-out test movies) before and after DeepInterpolation.