Unsupervised Hyperspectral Stimulated Raman Microscopy Image Enhancement: Denoising and Segmentation via One-Shot Deep Learning

Pedram Abdolghader; Andrew Ridsdale; Tassos Grammatikopoulos; Gavin Resch; Francois Legare; Albert Stolow; Adrian F. Pegoraro; Isaac Tamblyn

arXiv · 2104.08338

Unsupervised Hyperspectral Stimulated Raman Microscopy Image Enhancement: Denoising and Segmentation via One-Shot Deep Learning

Pedram Abdolghader·Andrew Ridsdale·Tassos Grammatikopoulos·Gavin Resch·Francois Legare·Albert Stolow·Adrian F. Pegoraro·Isaac Tamblyn

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Abstract

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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BibTeX RIS

Pedram Abdolghader, Andrew Ridsdale, Tassos Grammatikopoulos, Gavin Resch, Francois Legare, Albert Stolow, Adrian F. Pegoraro, Isaac Tamblyn. 2021-04-14. Unsupervised Hyperspectral Stimulated Raman Microscopy Image Enhancement: Denoising and Segmentation via One-Shot Deep Learning. https://arxiv.org/abs/2104.08338

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