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Optical focusing at depths in tissue is the Holy Grail of biomedical opticsthat may bring revolutionary advancement to the field. Wavefront shaping is awidely accepted approach to solve this problem, but most implementations thusfar have only operated with stationary media which, however, are scarcelyexistent in practice. In this article, we propose to apply a deep convolutionalneural network named as ReFocusing-Optical-Transformation-Net (RFOTNet), whichis a Multi-input Single-output network, to tackle the grand challenge of lightfocusing in nonstationary scattering media. As known, deep convolutional neuralnetworks are intrinsically powerful to solve inverse scattering problemswithout complicated computation. Considering the optical speckles of the mediumbefore and after moderate perturbations are correlated, an optical focus can berapidly recovered based on fine-tuning of pre-trained neural networks,significantly reducing the time and computational cost in refocusing. Thefeasibility is validated experimentally in this work. The proposed deeplearning-empowered wavefront shaping framework has great potentials infacilitating optimal optical focusing and imaging in deep and dynamic tissue.

Towards smart optical focusing: Deep learning-empowered wavefront shaping in nonstationary scattering media