# Spectroscopic data de-noising via training-set-free deep learning method

arXiv: https://arxiv.org/abs/2210.10494 · HTML: https://mlsanity.com/paper/2210.10494/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [On instabilities of deep learning in image reconstruction - Does AI come at a cost?](https://mlsanity.com/paper/1902.05300/) (2019)
- [Deep learning-based statistical noise reduction for multidimensional spectral data](https://mlsanity.com/paper/2107.00844/) (2021)
- [Super Resolution Convolutional Neural Network for Feature Extraction in Spectroscopic Data](https://mlsanity.com/paper/2001.10908/) (2020)

## Built on by (most cited first, 1 of 1)

- [Removing grid structure in angle-resolved photoemission spectra via deep learning method](https://mlsanity.com/paper/2210.11200/) (2022)

## Abstract (arXiv)

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.