Deep learning-enhanced fluorescence microscopy via degeneration decoupling.

Deep learning-based reconstruction has emerged as an effective tool in fluorescence microscopy, with the potential to resolve diffraction-limited structures. However, most deep-learning reconstruction methods employed an end-to-end strategy, which ignored physical laws in the imaging process and made the preparation of training data highly challenging as well. In this study, we have proposed a novel deconvolution algorithm based on an imaging model, deep-learning priors and the alternating direction method of multipliers. This scheme decouples the reconstruction into two separate sub-problems, one for deblurring and one for restraining noise and artifacts. As a result of the decoupling, we are able to introduce deep-learning image priors and a variance stabilizing transform against targeted image degeneration due to the low photon budget. Deep-learning priors are learned from the general image dataset, in which biological images do not have to be involved, and are more powerful than hand-designed ones. Moreover, the use of the imaging model ensures high fidelity and generalization. Experiments on various kinds of measurement data show that the proposed algorithm outperforms existing state-of-the-art deconvolution algorithms in resolution enhancement and generalization.