  Fluorescence microscopy is essential to study biological structures and
dynamics. However, existing systems suffer from a tradeoff between
field-of-view (FOV), resolution, and complexity, and thus cannot fulfill the
emerging need of miniaturized platforms providing micron-scale resolution
across centimeter-scale FOVs. To overcome this challenge, we developed
Computational Miniature Mesoscope (CM$^2$) that exploits a computational
imaging strategy to enable single-shot 3D high-resolution imaging across a wide
FOV in a miniaturized platform. Here, we present CM$^2$ V2 that significantly
advances both the hardware and computation. We complement the 3$\times$3
microlens array with a new hybrid emission filter that improves the imaging
contrast by 5$\times$, and design a 3D-printed freeform collimator for the LED
illuminator that improves the excitation efficiency by 3$\times$. To enable
high-resolution reconstruction across the large imaging volume, we develop an
accurate and efficient 3D linear shift-variant (LSV) model that characterizes
the spatially varying aberrations. We then train a multi-module deep learning
model, CM$^2$Net, using only the 3D-LSV simulator. We show that CM$^2$Net
generalizes well to experiments and achieves accurate 3D reconstruction across
a $\sim$7-mm FOV and 800-$\mu$m depth, and provides $\sim$6-$\mu$m lateral and
$\sim$25-$\mu$m axial resolution. This provides $\sim$8$\times$ better axial
localization and $\sim$1400$\times$ faster speed as compared to the previous
model-based algorithm. We anticipate this simple and low-cost computational
miniature imaging system will be impactful to many large-scale 3D fluorescence
imaging applications.
