Raman hyperspectral imaging (RHSI) is a valuable tool for gaining crucial information about the chemical composition of materials. However, obtaining clear Raman signals is not always a trivial task. Raw Raman signals can be susceptible to photoluminescence interference and noise. Hence, the preprocessing of RHSI is a required step for an effective and reliable chemical analysis. The main challenge is splitting the measured RHSI into separate Raman photoluminescence signals. Since no golden-standard exists, it is non-trivial to validate the correctness of the separated signals. While current state-of-the-art preprocessing methods are effective, they require expert knowledge and involve unintuitive hyperparameters. Current approaches also lack generalizability, requiring extensive hyperparameter tuning on a case-by-case basis, while even then results are not always as expected. To this end, this work proposes a novel iterative RHSI preprocessing pipeline for splitting raw Raman signals and noise removal based on linear spline and radial basis function regression (IlsaRBF). The proposed method involves hyperparameters based on the physical properties of Raman spectroscopy, making them intuitive to use. This leads to more robust and stable hyperparameters, reducing the necessity for extensive hyperparameter tuning. A thorough evaluation shows that the proposed method outperforms the current state-of-the-art. Additionally, a cosmic ray identification and removal algorithm (CRIR) and dynamic PCA for noise reduction are introduced. A standalone tool containing our proposed methods is provided, making RHSI preprocessing available to a broader audience, aiding further research and advancements in the field of Raman spectroscopy.

中文翻译：

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拉曼高光谱图像中的拉曼和光致发光信号分离，包括降噪

拉曼高光谱成像 (RHSI) 是获取有关材料化学成分的重要信息的宝贵工具。然而，获得清晰的拉曼信号并不总是一项简单的任务。原始拉曼信号可能容易受到光致发光干扰和噪声的影响。因此，RHSI 的预处理是有效且可靠的化学分析的必要步骤。主要挑战是将测得的 RHSI 分成单独的拉曼光致发光信号。由于不存在黄金标准，因此验证分离信号的正确性并非易事。虽然当前最先进的预处理方法是有效的，但它们需要专业知识并且涉及不直观的超参数。目前的方法还缺乏通用性，需要根据具体情况进行广泛的超参数调整，但即使如此，结果也并不总是符合预期。为此，这项工作提出了一种新颖的迭代 RHSI 预处理管道，用于基于线性样条和径向基函数回归（IlsaRBF）分割原始拉曼信号和噪声去除。所提出的方法涉及基于拉曼光谱物理特性的超参数，使其使用直观。这会带来更强大、更稳定的超参数，从而减少大量超参数调整的必要性。彻底的评估表明，所提出的方法优于当前最先进的方法。此外，还引入了宇宙射线识别和去除算法（CRIR）以及用于降噪的动态PCA。提供了包含我们提出的方法的独立工具，使 RHSI 预处理可供更广泛的受众使用，有助于拉曼光谱领域的进一步研究和进步。