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Memory-efficient Learning for Large-scale Computational Imaging
Critical aspects of computational imaging systems, such as experimental design and image priors, can be optimized through deep networks formed by the unrolled iterations of classical model-based reconstructions (termed physics-based networks).
Memory-efficient Learning for Large-scale Computational Imaging -- NeurIPS deep inverse workshop
Computational imaging systems jointly design computation and hardware to retrieve information which is not traditionally accessible with standard imaging systems.
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