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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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NIR-to-RGB image colorization based on a conditional GAN.

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    This summary is machine-generated.

    This study introduces a new generative adversarial network (GAN) for near-infrared (NIR) image colorization, significantly improving image quality and semantic understanding. The novel approach enhances color accuracy, detail clarity, and overall visual readability for NIR imagery.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Near-infrared (NIR) image colorization aims to enhance image readability and semantic information.
    • Current techniques suffer from color distortion, semantic ambiguity, and unclear texture.
    • Generative Adversarial Networks (GANs) offer a promising framework for image enhancement tasks.

    Purpose of the Study:

    • To propose a novel GAN-based approach for NIR image colorization.
    • To address limitations of existing methods, including color distortion and semantic ambiguity.
    • To improve the overall quality and interpretability of NIR images.

    Main Methods:

    • Developed a GAN with optimized generator and discriminator architectures.
    • Integrated a Res-WTConv-U-Net generator with a deep bottleneck block (residual block + efficient attention module).
    • Replaced traditional convolution with wavelet convolution (WTconv) for enhanced feature extraction.
    • Employed a dual-scale discriminator considering global structure and local details.
    • Utilized structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and color histogram similarity (CHS) for evaluation.

    Main Results:

    • Achieved an average PSNR improvement of 12.6%.
    • Demonstrated an average SSIM improvement of 7.4%.
    • Showcased an average CHS improvement of 9.5% compared to existing methods.
    • Experimental results on two datasets confirmed superior colorization effects.

    Conclusions:

    • The proposed GAN-based method significantly enhances NIR image colorization.
    • The novel network architecture and loss function effectively address color distortion and semantic ambiguity.
    • The approach provides superior performance in terms of image quality and detail preservation.