# FPM-INR: Fourier ptychographic microscopy image stack reconstruction using implicit neural representations

arXiv: https://arxiv.org/abs/2310.18529 · HTML: https://mlsanity.com/paper/2310.18529/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2023.

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

- [MicroDiffusion: Implicit Representation-Guided Diffusion for 3D Reconstruction from Limited 2D Microscopy Projections](https://mlsanity.com/paper/2403.10815/) (2024)
- [Neural Poisson Solver: A Universal and Continuous Framework for Natural Signal Blending](https://mlsanity.com/paper/2407.08457/) (2024)
- [Coordinate-based Neural Network for Fourier Phase Retrieval](https://mlsanity.com/paper/2311.14925/) (2023)
- [ConVRT: Consistent Video Restoration Through Turbulence with Test-time Optimization of Neural Video Representations](https://mlsanity.com/paper/2312.04679/) (2023)

## Abstract (arXiv)

Image stacks provide invaluable 3D information in various biological and pathological imaging applications. Fourier ptychographic microscopy (FPM) enables reconstructing high-resolution, wide field-of-view image stacks without z-stack scanning, thus significantly accelerating image acquisition. However, existing FPM methods take tens of minutes to reconstruct and gigabytes of memory to store a high-resolution volumetric scene, impeding fast gigapixel-scale remote digital pathology. While deep learning approaches have been explored to address this challenge, existing methods poorly generalize to novel datasets and can produce unreliable hallucinations. This work presents FPM-INR, a compact and efficient framework that integrates physics-based optical models with implicit neural representations (INR) to represent and reconstruct FPM image stacks. FPM-INR is agnostic to system design or sample types and does not require external training data. In our demonstrated experiments, FPM-INR substantially outperforms traditional FPM algorithms with up to a 25-fold increase in speed and an 80-fold reduction in memory usage for continuous image stack representations.