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Superpixels Meet Essential Spectra for Fast Raman Hyperspectral Microimaging.

Valentin Gilet, Guillaume Mabilleau,Matthieu Loumaigne,Laureen Coic,Raffaele Vitale,Thomas Oberlin, Joseh Enrique De Morais Goulart,Nicolas Dobigeon,Cyril Ruckebusch,David Rousseau

Optics Express(2024)SCI 2区

Univ Angers | Univ Lille | Univ Toulouse

Cited 0|Views19
Abstract
In the context of spectral unmixing, essential information corresponds to the most linearly dissimilar rows and/or columns of a two-way data matrix which are indispensable to reproduce the full data matrix in a convex linear way. Essential information has recently been shown accessible on-the-fly via a decomposition of the measured spectra in the Fourier domain and has opened new perspectives for fast Raman hyperspectral microimaging. In addition, when some spatial prior is available about the sample, such as the existence of homogeneous objects in the image, further acceleration for the data acquisition procedure can be achieved by using superpixels. The expected gain in acquisition time is shown to be around three order of magnitude on simulated and real data with very limited distortions of the estimated spectrum of each object composing the images.
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Hyperspectral Imaging,In Vivo Imaging,Infrared Spectroscopy,High-Resolution Imaging,Near-Infrared Spectroscopy
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要点】:本文提出了一种结合超像素与必要光谱分解技术,实现了快速拉曼超光谱显微成像,显著提高了数据采集速度且保证了估计光谱的准确性。

方法】:作者采用了一种基于傅里叶域的光谱分解方法来获取必要信息,并结合超像素技术利用样本的空间先验信息。

实验】:通过模拟和实际数据测试,该技术能够在保证非常有限失真的前提下,将数据采集时间提高大约三个数量级。具体数据集名称在文中未提及。