Computational imaging is a new frontier of imaging technology that overcomes fundamental limitations of conventional systems by jointly designing optics, devices, signal processing, and algorithms. In this talk, I will first present recent advancements in high-throughput computational microscopy based on coded illumination and nonlinear phase retrieval, which enables wide field-of-view and high-resolution Gigapixel and 3D phase microscopy capability. Next, I will present a new neural network inspired framework for solving inverse scattering problems using recursive models to enable recovery of multiple scattering information in large-scale complex media. Such computational imaging approach creates significant new capabilities by integrating hardware and computation at the system level. It promises wide applications, such as biomedicine, metrology, and remote sensing.
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