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    Area of Science:

    • Combustion Science
    • Optical Engineering
    • Artificial Intelligence

    Background:

    • Traditional RGB imaging offers limited spectral resolution for accurate flame temperature measurement.
    • Existing methods struggle with the low spectral detail inherent in three-band radiation images.

    Purpose of the Study:

    • To develop a multispectral reconstruction method for flame color images to improve temperature measurement accuracy.
    • To overcome the limitations of low spectral resolution in RGB-based temperature diagnostics.
    • To enable dynamic flame temperature field monitoring.

    Main Methods:

    • A synchronized imaging system captured candle flame images using both RGB and 25-band multispectral cameras.
    • K-means clustering and backpropagation neural networks (BPNN) were employed for spectral reconstruction.
    • Image partitioning created a training set linking RGB and multispectral responses for neural network training.

    Main Results:

    • The spectral reconstruction achieved an average relative error below 5%.
    • Temperature inversion yielded an average error of 31.5 K with a mean error of 1.79%.
    • High model accuracy was confirmed with test set R² values ranging from 0.97 to 0.99.

    Conclusions:

    • The proposed method effectively merges the spatial resolution of RGB images with the spectral detail of multispectral data.
    • This approach offers a novel and accurate method for dynamic flame temperature field monitoring.
    • The study demonstrates the feasibility of using AI-driven spectral reconstruction for combustion diagnostics.