For synthetic bead data (2D), 100 pairs of noisy and GT images were used for training the CARE algorithm, using a patch size of 128 × 128 pixels. N2V networks were trained on images with high noise levels (σ = 0.5), using a patch size of 64 × 64 pixels, 100 epochs, and 100 steps per epoch.

For restoration of chromatin microdomain images, dedicated CARE networks were trained for each exposure time condition (1, 3, 10 ms) using pairs of cropped images taken from fixed cells. These training pairs were obtained by alternately imaging at the target exposure time (e.g., 3 ms) and an exposure time sufficient to achieve a high SNR (e.g., 300 ms) for 100 times and subsequently cropping image stacks centered at the grid of photoactivated chromatin microdomains. The parameters for patch size (28 × 28 pixels) and samples per image (64) were chosen by applying the Bayesian optimization implementation by Nogueira (2014) blue right-pointing triangle. This method initially randomly samples the hypothesis space and then fits Gaussian processes to the observations. An acquisition function then determines the next point in the parameter space that would improve this model of the parameter space the most. Using this active learning approach, near-optimal parameter values are found without having to exhaustively search the parameter space. In our application, the parameter space is 2D with the number of samples taken per image as one, and the side length of the samples as the other dimension. The reward function of the optimizer is to find the set of parameters that minimize cumulative tracking error. We calculated tracking error as pixels per microdomain per frame: we summed the magnitude of the difference between the motion vector tracked in the ground truth and the motion vector tracked in the denoised image for each frame delta across all spots and then divided by the number of frame deltas and spots.

N2V networks for use with the chromatin microdomain images were trained on the cropped target image stacks directly, with one network trained for each image stack. We evaluated performance on training N2V networks on the full images (2048 × 2048 pixels) but found no improvement. Rather, training on the full images significantly increased training time. For denoising with structN2V, we first assessed the spatial autocorrelation of the noise using the MATLAB autocorr2d function and single images that were away from any cells. For both CARE and N2V we used a 90%–10% train-validation split.

To restore single-nucleosome images, a CARE network was trained using 100 pairs of fixed-cell images, captured at 1 s and 10 ms. N2V was trained using stacks of fixed images (100; 10 ms exposure). We optimized patch size (128 × 128 pixels for CARE and 64 × 64 for N2V) before training the networks. Because we had high-quality fixed-cell images (1 s exposure) as the GT, we were able to decide on the most accurate network.

For yeast genomic loci and spindle pole bodies, images were processed using 3D N2V to generate denoised time lapses. For both simulated genomic loci and spindle pole body time lapses, one N2V network was trained for each observation, with a patch size of 32, 32, and 4 pixels for X, Y, and Z, respectively.

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