无监督高光谱受激拉曼显微镜图像增强：通过One-Shot深度学习进行去噪和分割
Unsupervised Hyperspectral Stimulated Raman Microscopy Image Enhancement: Denoising and Segmentation via One-Shot Deep Learning
摘要：
高光谱受激拉曼散射（SRS）显微术是一种用于生物医学和矿物学成像的无标记技术，其可能遭受低信噪比。在这里，我们演示了使用无监督深度学习神经网络对SRS图像进行快速自动去噪：UHRED（无监督高光谱分辨率增强和去噪）。UHRED能够一次性学习;只需要一张高光谱图像，不需要在先前标记的数据集或图像上进行训练。此外，通过将k均值聚类算法应用于处理后的数据，我们展示了自动的、无监督的图像分割，在没有样本先验知识的情况下，产生直观的化学物种图，如锂矿石样本所示。
英文摘要：
Hyperspectral stimulated Raman scattering (SRS) microscopy is a label-free technique for biomedical and mineralogical imaging which can suffer from low signal to noise ratios. Here we demonstrate the use of an unsupervised deep learning neural network for rapid and automatic denoising of SRS images: UHRED (Unsupervised Hyperspectral Resolution Enhancement and Denoising). UHRED is capable of one-shot learning; only one hyperspectral image is needed, with no requirements for training on previously labelled datasets or images. Furthermore, by applying a k-means clustering algorithm to the processed data, we demonstrate automatic, unsupervised image segmentation, yielding, without prior knowledge of the sample, intuitive chemical species maps, as shown here for a lithium ore sample.
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