Unsupervised Full-color Cellular Image Reconstruction through Disordered Optical Fiber

TL;DR

This paper presents a novel unsupervised method for full-color cellular imaging through disordered optical fibers, enabling high-resolution, flexible, and robust imaging without paired training data, using a two-stage reconstruction process.

Contribution

It introduces an unsupervised full-color cellular imaging technique via disordered fibers, combining pixel-wise standardization and GAN-based detail recovery, eliminating the need for paired training data.

Findings

Achieves high-fidelity cellular imaging within 4 mm distance.

Demonstrates robustness when fiber is bent at 60°.

Shows improved generality with diverse object sets.

Abstract

Recent years have witnessed the tremendous development of fusing fiber-optic imaging with supervised deep learning to enable high-quality imaging of hard-to-reach areas. Nevertheless, the supervised deep learning method imposes strict constraints on fiber-optic imaging systems, where the input objects and the fiber outputs have to be collected in pairs. To unleash the full potential of fiber-optic imaging, unsupervised image reconstruction is in demand. Unfortunately, neither optical fiber bundles nor multimode fibers can achieve a point-to-point transmission of the object with a high sampling density, as is a prerequisite for unsupervised image reconstruction. The recently proposed disordered fibers offer a new solution based on the transverse Anderson localization. Here, we demonstrate unsupervised full-color imaging with a cellular resolution through a meter-long disordered fiber in both transmission and reflection modes. The unsupervised image reconstruction consists of two stages. In the first stage, we perform a pixel-wise standardization on the fiber outputs using the statistics of the objects. In the second stage, we recover the fine details of the reconstructions through a generative adversarial network. Unsupervised image reconstruction does not need paired images, enabling a much more flexible calibration under various conditions. Our new solution achieves full-color high-fidelity cell imaging within a working distance of at least 4 mm by only collecting the fiber outputs after an initial calibration. High imaging robustness is also demonstrated when the disordered fiber is bent with a central angle of 60{\deg}. Moreover, the cross-domain generality on unseen objects is shown to be enhanced with a diversified object set.