# Deep learning-based statistical noise reduction for multidimensional spectral data

arXiv: https://arxiv.org/abs/2107.00844 · HTML: https://mlsanity.com/paper/2107.00844/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 2 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Super Resolution Convolutional Neural Network for Feature Extraction in Spectroscopic Data](https://mlsanity.com/paper/2001.10908/) (2020)
- [CRYSPNet: Crystal Structure Predictions via Neural Network](https://mlsanity.com/paper/2003.14328/) (2020)

## Built on by (most cited first, 9 of 9)

- [Weak-signal extraction enabled by deep-neural-network denoising of diffraction data](https://mlsanity.com/paper/2209.09247/) (2022)
- [Denoising and feature extraction in photoemission spectra with variational auto-encoder neural networks](https://mlsanity.com/paper/2203.07537/) (2022)
- [Transfer Learning Application of Self-supervised Learning in ARPES](https://mlsanity.com/paper/2208.10893/) (2022)
- [PGNAA Spectral Classification of Metal with Density Estimations](https://mlsanity.com/paper/2208.13836/) (2022)
- [Spectroscopic data de-noising via training-set-free deep learning method](https://mlsanity.com/paper/2210.10494/) (2022)
- [Removing grid structure in angle-resolved photoemission spectra via deep learning method](https://mlsanity.com/paper/2210.11200/) (2022)
- [Deep learning-based denoising for fast time-resolved flame emission spectroscopy in high-pressure combustion environment](https://mlsanity.com/paper/2208.12544/) (2022)
- [Training-set-free two-stage deep learning for spectroscopic data de-noising](https://mlsanity.com/paper/2402.18830/) (2024)
- [PGNAA Spectral Classification of Aluminium and Copper Alloys with Machine Learning](https://mlsanity.com/paper/2404.14107/) (2024)

## Abstract (arXiv)

In spectroscopic experiments, data acquisition in multi-dimensional phase space may require long acquisition time, owing to the large phase space volume to be covered. In such case, the limited time available for data acquisition can be a serious constraint for experiments in which multidimensional spectral data are acquired. Here, taking angle-resolved photoemission spectroscopy (ARPES) as an example, we demonstrate a denoising method that utilizes deep learning as an intelligent way to overcome the constraint. With readily available ARPES data and random generation of training data set, we successfully trained the denoising neural network without overfitting. The denoising neural network can remove the noise in the data while preserving its intrinsic information. We show that the denoising neural network allows us to perform similar level of second-derivative and line shape analysis on data taken with two orders of magnitude less acquisition time. The importance of our method lies in its applicability to any multidimensional spectral data that are susceptible to statistical noise.