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    Area of Science:

    • Computer Vision
    • Image Processing
    • Deep Learning

    Background:

    • Invertible grayscale allows color recovery from grayscale images.
    • Previous methods suffered from visual artifacts and JPEG compression sensitivity.

    Purpose of the Study:

    • To improve invertible grayscale techniques by addressing visual artifacts and JPEG compression issues.
    • To develop a robust method for hiding and restoring color information in grayscale images.

    Main Methods:

    • Introduced adversarial training with auxiliary adversarial networks.
    • Incorporated a JPEG simulator for robust online training.
    • Employed end-to-end training of hiding and restoring sub-networks.

    Main Results:

    • Significantly reduced visual artifacts in smooth regions of restored images.
    • Achieved superior qualitative and quantitative results compared to prior work.
    • Demonstrated robustness against JPEG compression for color information recovery.

    Conclusions:

    • The proposed adversarial training and JPEG simulation enhance invertible grayscale functionality.
    • The method offers improved visual quality and robustness, making it practical for real-world applications.
    • The framework is adaptable to various grayscale constraints, yielding excellent outcomes.