论文标题
光谱数据通过无训练集的深度学习方法拖延了噪声
Spectroscopic data de-noising via training-set-free deep learning method
论文作者
论文摘要
去噪声在光谱后处理中起着至关重要的作用。基于机器学习的方法在从嘈杂的数据中提取固有信息方面表现出良好的性能，但是通常需要一个高质量的训练集，在实际实验测量中通常无法访问。在这里，使用角度分辨光发射光谱（ARPE）中的光谱为例，我们开发了一种推定的方法来提取固有的光谱信息，而无需训练集。这是可能的，因为我们的方法利用了光谱本身的自相关信息。它保留了内在的能频带特征，从而促进了进一步的分析和处理。此外，由于我们的方法与以前的方法相比不受培训集的特定特性的限制，因此它很可能扩展到其他领域和应用程序方案，在这些领域和应用程序方案中，获得高质量的多维培训数据是具有挑战性的。
De-noising plays a crucial role in the post-processing of spectra. Machine learning-based methods show good performance in extracting intrinsic information from noisy data, but often require a high-quality training set that is typically inaccessible in real experimental measurements. Here, using spectra in angle-resolved photoemission spectroscopy (ARPES) as an example, we develop a de-noising method for extracting intrinsic spectral information without the need for a training set. This is possible as our method leverages the self-correlation information of the spectra themselves. It preserves the intrinsic energy band features and thus facilitates further analysis and processing. Moreover, since our method is not limited by specific properties of the training set compared to previous ones, it may well be extended to other fields and application scenarios where obtaining high-quality multidimensional training data is challenging.
