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RSS FeedsEnergies, Vol. 15, Pages 5747: Numerical Demonstration of Unsupervised-Learning-Based Noise Reduction in Two-Dimensional Rayleigh Imaging (Energies)

 
 

8 august 2022 12:24:00

 
Energies, Vol. 15, Pages 5747: Numerical Demonstration of Unsupervised-Learning-Based Noise Reduction in Two-Dimensional Rayleigh Imaging (Energies)
 


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.


 
110 viewsCategory: Biophysics, Biotechnology, Physics
 
Energies, Vol. 15, Pages 5746: Volume-of-Fluid Based Finite-Volume Computational Simulations of Three-Phase Nanoparticle-Liquid-Gas Boiling Problems in Vertical Rectangular Channels (Energies)
Energies, Vol. 15, Pages 5749: Intelligent Action Planning for Well Construction Operations Demonstrated for Hole Cleaning Optimization and Automation (Energies)
 
 
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