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Color Vision01:24

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Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
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Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
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Dual Color Space Guided Sketch Colorization.

Zhi Dou, Ning Wang, Baopu Li

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    This study introduces a dual color space guided generative adversarial network (DCSGAN) for automatic sketch colorization. The method enhances image vividness and reduces artifacts by leveraging both RGB and HSV color spaces for improved results.

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    Area of Science:

    • Computer Graphics
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Automatic sketch colorization requires generating color, texture, and shading from abstract sketches.
    • Existing methods often produce dull colors and artifacts by focusing solely on the RGB color space.
    • The HSV color space aligns better with human visual perception and can improve colorization quality.

    Purpose of the Study:

    • To propose a novel sketch colorization method that incorporates dual color spaces (RGB and HSV).
    • To address limitations of existing methods, such as dull colors and artifacts.
    • To generate more vivid and aesthetically pleasing colorized images from sketches.

    Main Methods:

    • Developed a dual color space guided generative adversarial network (DCSGAN).
    • Incorporated a color space transformation (CST) network for RGB to HSV conversion.
    • Introduced a drawing priori (DP) loss for pixel-level supervision and a dual color space adversarial (DCSA) loss for global guidance.

    Main Results:

    • The DCSGAN method demonstrates superior performance compared to state-of-the-art methods.
    • Achieved enhanced color vividness and reduced artifacts in sketch colorization.
    • The dual color space approach effectively utilizes complementary information from both RGB and HSV.

    Conclusions:

    • The proposed DCSGAN method offers a significant advancement in automatic sketch colorization.
    • Integrating HSV color space supervision alongside RGB leads to higher quality and more visually appealing results.
    • The method effectively balances pixel-level detail and global aesthetic consistency.