Learned Single-Pixel Fluorescence Microscopy

TL;DR

This paper introduces a learned autoencoder approach for single-pixel fluorescence microscopy that significantly improves reconstruction speed and quality, enabling multispectral imaging and potential biomedical applications.

Contribution

It presents a novel self-supervised learning method to optimize measurement and reconstruction in single-pixel microscopy, surpassing traditional techniques in speed and quality.

Findings

Reduces reconstruction time by two orders of magnitude

Achieves superior image quality compared to traditional methods

Enables multispectral fluorescence imaging

Abstract

Single-pixel imaging has emerged as a key technique in fluorescence microscopy, where fast acquisition and reconstruction are crucial. In this context, images are reconstructed from linearly compressed measurements. In practice, total variation minimisation is still used to reconstruct the image from noisy measurements of the inner product between orthogonal sampling pattern vectors and the original image data. However, data can be leveraged to learn the measurement vectors and the reconstruction process, thereby enhancing compression, reconstruction quality, and speed. We train an autoencoder through self-supervision to learn an encoder (or measurement matrix) and a decoder. We then test it on physically acquired multispectral and intensity data. During acquisition, the learned encoder becomes part of the physical device. Our approach can enhance single-pixel imaging in fluorescence microscopy by reducing reconstruction time by two orders of magnitude, achieving superior image quality, and enabling multispectral reconstructions. Ultimately, learned single-pixel fluorescence microscopy could advance diagnosis and biological research, providing multispectral imaging at a fraction of the cost.