# Deep learning-based denoising for fast time-resolved flame emission spectroscopy in high-pressure combustion environment

arXiv: https://arxiv.org/abs/2208.12544 · HTML: https://mlsanity.com/paper/2208.12544/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 29 Jul 2022.

## Builds on (most cited first, 14 of 14)

- [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)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Noise2Noise: Learning Image Restoration without Clean Data](https://mlsanity.com/paper/1803.04189/) (2018)
- [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017)
- [Noise2Void - Learning Denoising from Single Noisy Images](https://mlsanity.com/paper/1811.10980/) (2018)
- [Noise2Self: Blind Denoising by Self-Supervision](https://mlsanity.com/paper/1901.11365/) (2019)
- [Toward Convolutional Blind Denoising of Real Photographs](https://mlsanity.com/paper/1807.04686/) (2018)
- [Deep Learning on Image Denoising: An overview](https://mlsanity.com/paper/1912.13171/) (2019)
- [Learning scale-variant and scale-invariant features for deep image classification](https://mlsanity.com/paper/1602.01255/) (2016)
- [Deep learning-based statistical noise reduction for multidimensional spectral data](https://mlsanity.com/paper/2107.00844/) (2021)
- [Using machine learning to construct velocity fields from OH-PLIF images](https://mlsanity.com/paper/1909.13669/) (2019)

## Abstract (arXiv)

A deep learning strategy is developed for fast and accurate gas property measurements using flame emission spectroscopy (FES). Particularly, the short-gated fast FES is essential to resolve fast-evolving combustion behaviors. However, as the exposure time for capturing the flame emission spectrum gets shorter, the signal-to-noise ratio (SNR) decreases, and characteristic spectral features indicating the gas properties become relatively weaker. Then, the property estimation based on the short-gated spectrum is difficult and inaccurate. Denoising convolutional neural networks (CNN) can enhance the SNR of the short-gated spectrum. A new CNN architecture including a reversible down- and up-sampling (DU) operator and a loss function based on proper orthogonal decomposition (POD) coefficients is proposed. For training and testing the CNN, flame chemiluminescence spectra were captured from a stable methane-air flat flame using a portable spectrometer (spectral range: 250 - 850 nm, resolution: 0.5 nm) with varied equivalence ratio (0.8 - 1.2), pressure (1 - 10 bar), and exposure time (0.05, 0.2, 0.4, and 2 s). The long exposure (2 s) spectra were used as the ground truth when training the denoising CNN. A kriging model with POD is trained by the long-gated spectra for calibration, and then the prediction of the gas properties taking the denoised short-gated spectrum as the input: The property prediction errors of pressure and equivalence ratio were remarkably lowered in spite of the low SNR attendant with reduced exposure.