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