# Deep-learning-augmented Computational Miniature Mesoscope

arXiv: https://arxiv.org/abs/2205.00123 · HTML: https://mlsanity.com/paper/2205.00123/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2022.

## Built on by (most cited first, 4 of 4)

- [Minimalist and High-Quality Panoramic Imaging with PSF-aware Transformers](https://mlsanity.com/paper/2306.12992/) (2023)
- [Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data](https://mlsanity.com/paper/2309.13457/) (2023)
- [Annular Computational Imaging: Capture Clear Panoramic Images through Simple Lens](https://mlsanity.com/paper/2206.06070/) (2022)
- [Local Conditional Neural Fields for Versatile and Generalizable Large-Scale Reconstructions in Computational Imaging](https://mlsanity.com/paper/2307.06207/) (2023)

## Abstract (arXiv)

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 (CM2) 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 CM2 V2 that significantly advances both the hardware and computation. We complement the 3×3 microlens array with a new hybrid emission filter that improves the imaging contrast by 5×, and design a 3D-printed freeform collimator for the LED illuminator that improves the excitation efficiency by 3×. 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, CM2Net, using only the 3D-LSV simulator. We show that CM2Net generalizes well to experiments and achieves accurate 3D reconstruction across a ~7-mm FOV and 800-μm depth, and provides ~6-μm lateral and ~25-μm axial resolution. This provides ~8× better axial localization and ~1400× 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.