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    Area of Science:

    • Computer Vision
    • Image Processing
    • Digital Photography

    Background:

    • Near-infrared (NIR) gray images paired with visible color images offer benefits for image restoration and classification.
    • Existing naive coloring methods struggle with discrepancies in brightness and structure between NIR and visible images, leading to unrealistic results.

    Purpose of the Study:

    • To develop a new, realistic coloring method for near-infrared gray images.
    • To address the limitations of naive coloring techniques by resolving discrepancies between NIR and visible image data.

    Main Methods:

    • A novel contrast-preserving mapping model was developed to adjust NIR images, matching their luminance to visible images while retaining original contrast and details.
    • A method was created to derive and transfer realistic colors from visible images to the adjusted NIR images using the proposed mapping model.

    Main Results:

    • The proposed method successfully preserves local contrast and image details from the original NIR images.
    • Realistic colors were effectively transferred from visible images to the processed NIR images.
    • The NIR coloring technique demonstrated efficacy in noise and haze removal and local contrast enhancement.

    Conclusions:

    • The developed contrast-preserving mapping model and coloring method provide a significant improvement for NIR image processing.
    • This technique enables realistic colorization of NIR images, enhancing their utility for various applications including image restoration and classification.