Deep learning-enhanced fluorescence microscopy via confocal physical imaging model.Confocal microscopy is one of the most widely used tools for high-resolution cellular, tissue imaging and industrial inspection. 
confocal physical imaging model
By combining the residual neural network with a lightweight feature attention module with degradation model of confocal microscopy ensures high fidelity and generalization. 
residual neural network
Experiments on different measured data report that compared with the two deconvolution algorithms, non-negative least squares algorithm and Richardson-Lucy algorithm, the structural similarity index between the network output image and the real image reaches a high level above 0.82, and the peak signal-to-noise ratio can be improved by more than 0.6 dB. 
non-negative least squares algorithm
Experiments on different measured data report that compared with the two deconvolution algorithms, non-negative least squares algorithm and Richardson-Lucy algorithm, the structural similarity index between the network output image and the real image reaches a high level above 0.82, and the peak signal-to-noise ratio can be improved by more than 0.6 dB. 
Richardson-Lucy algorithm