论文标题
实时和高通量拉曼信号提取和汽车的处理高光谱成像
Real-time and high-throughput Raman signal extraction and processing in CARS hyperspectral imaging
论文作者
论文摘要
对于（a）拉曼信号提取，（b）denoising，以及（c）相干反stokes反stokes拉曼散射（CARS）易发射成像和光谱成像学。这些新方法比传统方法快的速度更快，并且由于唯一的核心概念：以较小的基础向量集进行所有处理，并使用矩阵/矢量乘法来直接和快速转换整个数据集。在实验上，我们证明了鸡肉软骨的703026光谱图像可以在70 s（约0.1 ms / spectrum）中处理，这比传统工作流程（约7.0 ms / spectrum）快约70倍。此外，我们通过重新使用带有新数据的转换基矢量集来讨论该方法如何用于机器学习（ML）。使用此ML范式，与常规工作流程相比，在大约33 s中处理了相同的组织图像（训练后），这是大约150倍的速度。
We present a new collection of processing techniques, collectively "factorized Kramers--Kronig and error correction" (fKK-EC), for (a) Raman signal extraction, (b) denoising, and (c) phase- and scale-error correction in coherent anti-Stokes Raman scattering (CARS) hyperspectral imaging and spectroscopy. These new methods are orders-of-magnitude faster than conventional methods and are capable of real-time performance, owing to the unique core concept: performing all processing on a small basis vector set and using matrix/vector multiplication afterwards for direct and fast transformation of the entire dataset. Experimentally, we demonstrate that a 703026 spectra image of chicken cartilage can be processed in 70 s (approximately 0.1 ms / spectrum), which is approximately 70 times faster than with the conventional workflow (approximately 7.0 ms / spectrum). Additionally, we discuss how this method may be used for machine learning (ML) by re-using the transformed basis vector sets with new data. Using this ML paradigm, the same tissue image was processed (post-training) in approximately 33 s, which is a speed-up of approximately 150 times when compared with the conventional workflow.
