Unsupervised learning based noise reduction algorithm in 2D Rayleigh images

Minnan Cai; Jiawei Peng; Wenjiang Xu

doi:10.1117/12.2623104

22 December 2022 Unsupervised learning based noise reduction algorithm in 2D Rayleigh images

Minnan Cai, Jiawei Peng, Wenjiang Xu

Proceedings Volume 12459, Sixth International Symposium on Laser Interaction with Matter; 124590P (2022) https://doi.org/10.1117/12.2623104
Event: Sixth International Symposium on Laser Interaction with Matter (LIMIS 2022), 2022, Ningbo, China

Abstract

This work reports a novel image denoising and reconstruction algorithm based on unsupervised learning for removing Mie scattering interference in Rayleigh images. We first superimposed numerically simulated Rayleigh images and noise images acquired in experiment to generate noisy Rayleigh images as training data. The proposed unsupervised model was then trained based on unpaired datasets. Finally, extensive evaluations were conducted to demonstrate a convincing denoising result, which displayed an excellent reconstruction quality with a peak-signal-to-noise of ~41dB and an overall reconstruction error of ~0.5%. The results showed that our algorithm was able to provide an alternative method for noise reduction in two dimensional Rayleigh measurement of combustion.

Citation Download Citation

Minnan Cai, Jiawei Peng, and Wenjiang Xu "Unsupervised learning based noise reduction algorithm in 2D Rayleigh images", Proc. SPIE 12459, Sixth International Symposium on Laser Interaction with Matter, 124590P (22 December 2022); https://doi.org/10.1117/12.2623104

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