  Light scattering and aberrations limit optical microscopy in biological
tissue, which motivates the development of adaptive optics techniques. Here, we
develop a method for adaptive optics with reflected light and deep neural
networks compatible with an epi-detection configuration. Large datasets of
sample aberrations which consist of excitation and detection path aberrations
as well as the corresponding reflected focus images are generated. These
datasets are used for training deep neural networks. After training, these
networks can disentangle and independently correct excitation and detection
aberrations based on reflected light images recorded from scattering samples. A
similar deep learning approach is also demonstrated with scattering guide
stars. The predicted aberration corrections are validated using two photon
imaging.
