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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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Hint-Based Image Colorization Based on Hierarchical Vision Transformer.

Subin Lee1, Yong Ju Jung1

  • 1School of Computing, Gachon University, Seongnam 13120, Korea.

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|October 14, 2022
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Summary
This summary is machine-generated.

This study introduces a vision transformer network for hint-based image colorization, overcoming limitations of traditional CNNs. The transformer model effectively handles long-range dependencies, producing visually plausible results with minimal color hints.

Keywords:
attention mapdeep learningimage colorizationvision transformer

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Hint-based image colorization is an image-to-image translation task.
  • Traditional deep learning methods using Convolutional Neural Networks (CNNs) suffer from spatial locality, leading to artifacts like false color and color bleeding.

Purpose of the Study:

  • To propose a novel vision transformer-based colorization network to address limitations of CNN-based approaches.
  • To achieve visually plausible colorization results using minimal color hints.

Main Methods:

  • A hierarchical vision transformer architecture with an encoder-decoder structure based on transformer blocks was developed.
  • The network leverages transformer blocks to learn long-range dependencies in images.

Main Results:

  • The proposed transformer model outperforms conventional CNN-based models in hint-based image colorization.
  • Qualitative analysis confirmed the effectiveness of long-range dependency learning for improved colorization.

Conclusions:

  • Vision transformer networks offer a superior alternative to CNNs for hint-based image colorization.
  • The ability to capture long-range dependencies is crucial for generating high-quality, artifact-free colorized images.