用自监督学习优化荧光显微镜单像素成像，提速100倍且画质更好
Learned Single-Pixel Fluorescence Microscopy
- 通过自监督训练的自编码器学习测量矩阵与重建算法
- 重建速度提升100倍，多光谱图像质量显著优于传统方法
- 适合需要低成本、快速多光谱成像的生物研究与临床诊断
单像素成像已成为荧光显微镜中的关键技术，快速采集与重建至关重要。当前图像从线性压缩测量中重建，通常采用总变差最小化处理含噪测量数据，即原始图像与正交采样模式向量的内积。然而，可通过数据学习测量向量和重建过程，从而提升压缩率、重建质量与速度。本文训练一个自监督自编码器，学习编码器（测量矩阵）与解码器。在真实物理采集的多光谱与强度数据上测试该方法。采集时，学习得到的编码器嵌入物理设备中。该方法可使单像素荧光显微镜的重建时间降低两个数量级，实现更优图像质量，并支持多光谱重建。最终，学习型单像素荧光显微镜有望以极低成本推动诊断与生物研究进展。
原文摘要 · Abstract (English)
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.
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