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    This study introduces a novel image colorization method using superpixel segmentation, weighted nonlocal self-similarity, and local consistency. The approach effectively transfers color from a source image to grayscale images, outperforming existing techniques.

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    Area of Science:

    • Computer Vision
    • Image Processing

    Background:

    • Image colorization is challenging due to the lack of a one-to-one mapping between local texture and color.
    • Existing methods struggle with accurately assigning colors to grayscale images.

    Purpose of the Study:

    • To develop an effective image colorization technique leveraging superpixel segmentation.
    • To improve color transfer accuracy by incorporating weighted nonlocal self-similarity and local consistency.

    Main Methods:

    • Utilizing superpixel segmentation to analyze images at a finer resolution.
    • Extracting multi-level features from superpixels in both target grayscale and source color images.
    • Employing a top-down feature matching scheme with confidence assignment for color candidate selection.
    • Applying a variational approach to determine the optimal color for each superpixel.

    Main Results:

    • The proposed method demonstrates superior performance compared to state-of-the-art image colorization techniques.
    • Experimental results validate the effectiveness of the weighted nonlocal self-similarity and local consistency constraints.
    • The method shows robustness in transferring color information accurately.

    Conclusions:

    • The developed image colorization approach effectively addresses the challenges of color assignment.
    • The technique offers a significant improvement over existing methods for grayscale image colorization.
    • The framework is adaptable for color transfer tasks between two color images.