  Deep neural networks have been applied to improve the image quality of
fluorescence microscopy imaging. Previous methods are based on convolutional
neural networks (CNNs) which generally require more time-consuming training of
separate models for each new imaging experiment, impairing the applicability
and generalization. Once the model is trained (typically with tens to hundreds
of image pairs) it can then be used to enhance new images that are like the
training data. In this study, we proposed a novel imaging-transformer based
model, Convolutional Neural Network Transformer (CNNT), to outperform the CNN
networks for image denoising. In our scheme we have trained a single CNNT based
backbone model from pairwise high-low SNR images for one type of fluorescence
microscope (instance structured illumination, iSim). Fast adaption to new
applications was achieved by fine-tuning the backbone on only 5-10 sample pairs
per new experiment. Results show the CNNT backbone and fine-tuning scheme
significantly reduces the training time and improves the image quality,
outperformed training separate models using CNN approaches such as - RCAN and
Noise2Fast. Here we show three examples of the efficacy of this approach on
denoising wide-field, two-photon and confocal fluorescence data. In the
confocal experiment, which is a 5 by 5 tiled acquisition, the fine-tuned CNNT
model reduces the scan time form one hour to eight minutes, with improved
quality.
