We introduce a phase conjugation method that utilizes multiple incoherent guidestars to control light in complex media. The technique involves the characterization of mutually incoherent scattered fields, followed by their time-reversal. With this approach, we achieve precise light focusing on individual and multiple guidestars, as well as efficient energy delivery to an extended target through scattering media. This method has various potential applications, including optical manipulation, targeted stimulation and deep optical imaging.
We present a deep learning approach for the rapid resolution enhancement of optical diffraction tomography. Once our three-dimensional U-net-based convolutional neural network learns an image translation between raw tomograms and total-variation-regularized tomograms, the trained network can fill in the missing cone of a measured refractive index tomogram and improve its resolution within seconds. We demonstrate the feasibility and generalizability of our approach on various biological samples, including bacteria, WBC, and NIH3T3.
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