Model-driven neural network for compressive holographic reconstruction

Compressive holography enables multilayer reconstruction by single in-line hologram without twin image and crosstalk. However, the iterative reconstruction methods cannot meet the needs of rapid imaging. We proposed a model-driven neural network for compressive holographic reconstruction by combining the interpretability of iterative algorithm and fast inference of deep neural network. The experiment demonstrates the compelling reconstruction with fast computational speed.