DeepLeMiN: Deep-learning-empowered Physics-aware Lensless Miniscope

Mask-based lensless fluorescence microscopy is a compact, portable imaging technique promising for biomedical research. It forms images through a thin optical mask near the camera without bulky optics, enabling snapshot three-dimensional imaging and a scalable field of view (FOV) without increasing device thickness. Lensless microscopy relies on computational algorithms to solve the inverse problem of object reconstruction. However, there has been a lack of efficient reconstruction algorithms for large-scale data. Furthermore, the entire FOV is typically reconstructed as a whole, which demands substantial computational resources and limits the scalability of the FOV. Here, we developed DeepLeMiN, a lensless microscope with a custom designed optical mask and a multi-stage physics-informed deep learning model. This not only enables the reconstruction of localized FOVs, but also significantly reduces the computational resource demands and facilitates real-time reconstruction. Our deep learning algorithm can reconstruct object volumes over 4x6x0.6 mm3, achieving lateral and axial resolution of [~]10 {micro}m and [~]50 {micro}m respectively. We demonstrated significant improvement in both reconstruction quality and speed compared to traditional methods, across various fluorescent samples with dense structures. Notably, we achieved high-quality reconstruction of 3D motion of hydra and the neuronal activity with cellular resolution in awake mouse cortex. DeepLeMiN holds great promise for scalable, large FOV, real-time, 3D imaging applications with compact device footprint.