Computational Miniature Mesoscope (CM2) is a novel fluorescence imaging device that achieves single-shot 3D imaging on a compact platform by jointly designing the optics and algorithm. However, the low axial resolution and heavy computational cost hinder its biomedical applications. Here, we demonstrate a deep learning framework, termed CM2Net, to perform fast and reliable 3D reconstruction. Specifically, the multi-stage CM2Net is trained on synthetic data with realistic field varying aberrations based on a 3D linear shift variant model. We experimentally demonstrate that the CM2Net can provide 10x improvement in axial resolution and 1400x faster reconstruction speed.
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