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We introduce a multi-branch model-based architecture for image reconstruction in lensless imaging. The structure consists of two learning branches, namely a physical model-based network, and a data-driven network. It uses intermediate outputs from the former as a prior for guiding the learning of the reconstruction neural network, which mimics the mapping between the reconstructed high-resolution images and raw images. We demonstrate that the proposed architecture offers a flexible combination of model-based methods and deep networks with superior reconstruction performance than methods using only an unrolled optimization network or pure deep neural networks for image reconstruction.
Tianjiao Zeng andEdmund Y. Lam
"Model-based network architecture for image reconstruction in lensless imaging", Proc. SPIE 11551, Holography, Diffractive Optics, and Applications X, 115510B (10 October 2020); https://doi.org/10.1117/12.2575205
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Tianjiao Zeng, Edmund Y. Lam, "Model-based network architecture for image reconstruction in lensless imaging," Proc. SPIE 11551, Holography, Diffractive Optics, and Applications X, 115510B (10 October 2020); https://doi.org/10.1117/12.2575205