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    This study enhances low-light raw image denoising by reforming data to overcome learnability limitations. The strategy improves image quality and model performance by addressing noise and data issues.

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    Area of Science:

    • Computational photography
    • Image processing
    • Machine learning

    Background:

    • Learning-based methods are mainstream for low-light raw image denoising.
    • Current methods face learnability bottlenecks due to limited data, complex noise, and poor data quality.

    Purpose of the Study:

    • To introduce a learnability enhancement strategy for low-light raw image denoising.
    • To address the limitations of paired real data mapping.

    Main Methods:

    • Reforming paired real data using noise modeling.
    • Integrating shot noise augmentation (SNA) to increase data volume.
    • Implementing dark shading correction (DSC) to reduce noise complexity.
    • Developing an improved image acquisition protocol to enhance data quality.

    Main Results:

    • Shot noise augmentation (SNA) promotes data mapping precision.
    • Dark shading correction (DSC) enhances data mapping accuracy.
    • The developed image acquisition protocol improves data mapping reliability.
    • Experiments demonstrate the strategy's superiority on public and new datasets.

    Conclusions:

    • The proposed learnability enhancement strategy significantly improves low-light raw image denoising.
    • The integrated methods (SNA, DSC, new protocol) effectively overcome existing bottlenecks.
    • The new dataset facilitates further research in low-light image denoising.