  Optical focusing at depths in tissue is the Holy Grail of biomedical optics
that may bring revolutionary advancement to the field. Wavefront shaping is a
widely accepted approach to solve this problem, but most implementations thus
far have only operated with stationary media which, however, are scarcely
existent in practice. In this article, we propose to apply a deep convolutional
neural network named as ReFocusing-Optical-Transformation-Net (RFOTNet), which
is a Multi-input Single-output network, to tackle the grand challenge of light
focusing in nonstationary scattering media. As known, deep convolutional neural
networks are intrinsically powerful to solve inverse scattering problems
without complicated computation. Considering the optical speckles of the medium
before and after moderate perturbations are correlated, an optical focus can be
rapidly recovered based on fine-tuning of pre-trained neural networks,
significantly reducing the time and computational cost in refocusing. The
feasibility is validated experimentally in this work. The proposed deep
learning-empowered wavefront shaping framework has great potentials in
facilitating optimal optical focusing and imaging in deep and dynamic tissue.
