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End-to-End Optimization of Metasurfaces for Imaging with Compressed Sensing
We present a framework for the end-to-end optimization of metasurface imaging systems that reconstruct targets using compressed sensing, a technique for solving underdetermined imaging problems when the target object exhibits sparsity (i. e. the object can be described by a small number of non-zero values, but the positions of these values are unknown).
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