Numerical Demonstration of Unsupervised-Learning-Based Noise Reduction in Two-Dimensional Rayleigh Imaging

Minnan Cai, Hua Jin, Beichen Lin, Wenjiang Xu () and Yancheng You
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Minnan Cai: School of Aerospace Engineering, Xiamen University, Xiamen 361005, China
Hua Jin: School of Aerospace Engineering, Xiamen University, Xiamen 361005, China
Beichen Lin: School of Aerospace Engineering, Xiamen University, Xiamen 361005, China
Wenjiang Xu: School of Aerospace Engineering, Xiamen University, Xiamen 361005, China
Yancheng You: School of Aerospace Engineering, Xiamen University, Xiamen 361005, China

Energies, 2022, vol. 15, issue 15, 1-15

Abstract: The conventional denoising method in Rayleigh imaging in a general sense requires an additional hardware investment and the use of the underlying physics. This work demonstrates an alternative image denoising reconstruction model based on unsupervised learning that aims to remove Mie scattering and shot noise interference from two-dimensional (2D) Rayleigh images. The model has two generators and two discriminators whose parameters can be trained with either feature-paired or feature-unpaired data independently. The proposed network was extensively evaluated with a qualitative examination and quantitative metrics, such as PSNR, ER, and SSIM. The results demonstrate that the feature-paired training network exhibits a better performance compared with several other networks reported in the literature. Moreover, when the flame features are not paired, the feature-unpaired training network still yields a good agreement with ground truth data. Specific indicators of the quantitative evaluation show a promising denoising ability with a peak signal-to-noise ratio of ~37 dB, an overall reconstruction error of ~1%, and a structure similarity index of ~0.985. Additionally, the pre-trained unsupervised model based on unpaired training can be generalized to denoise Rayleigh images with extra noise or a different Reynolds number without updating the model parameters.

Keywords: unsupervised learning; noise reduction; Rayleigh imaging (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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