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Deconvolution is the most commonly used image processing method to remove theblur caused by the point-spread-function (PSF) in optical imaging systems.While this method has been successful in deblurring, it suffers from severaldisadvantages including being slow, since it takes many iterations, suboptimal,in cases where experimental operator chosen to represent PSF is not optimal. Inthis paper, we are proposing a deep-learning-based deblurring method applicableto optical microscopic imaging systems. We tested the proposed method indatabase data, simulated data, and experimental data (include 2D opticalmicroscopic data and 3D photoacoustic microscopic data), all of which showedmuch improved deblurred results compared to deconvolution. To quantify theimproved performance, we compared our results against several deconvolutionmethods. Our results are better than conventional techniques and do not requiremultiple iterations or pre-determined experimental operator. Our method has theadvantages of simple operation, short time to compute, good deblur results andwide application in all types of optical microscopic imaging systems. The deeplearning approach opens up a new path for deblurring and can be applied invarious biomedical imaging fields.

A New Deep Learning Method for Image Deblurring in Optical Microscopic Systems