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Color Vision

Color Vision

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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Related Experiment Video

Updated: Jul 23, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Unsupervised Deep Exemplar Colorization via Pyramid Dual Non-Local Attention.

Hanzhang Wang, Deming Zhai, Xianming Liu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|July 13, 2023

Summary

This summary is machine-generated.

This study introduces a novel exemplar-based colorization method using a pyramid dual non-local attention network. The approach effectively transfers color styles and semantic information, achieving state-of-the-art photo-realistic results.

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Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Exemplar-based colorization aims to apply colors from a reference image to a grayscale target image, preserving semantic content and color style.
Existing methods struggle with effectively utilizing semantic color information and integrating it into the target image, often losing spatial details due to single-stage architectures.

Purpose of the Study:

To develop an advanced exemplar-based colorization strategy that overcomes limitations of current methods.
To enhance the exploitation of global color style and semantic information from reference images.
To improve the fusion mechanism for better semantic consistency and detail preservation.

Main Methods:

Proposed an exemplar colorization strategy utilizing a pyramid dual non-local attention network to capture long-range dependencies and multi-scale correlations.
Implemented symmetrical branches for feature alignment between target and reference images, coupled with a bidirectional non-local fusion strategy.
Employed an unsupervised learning approach with hybrid supervision (pseudo-paired and unpaired).

Main Results:

The proposed method demonstrates superior performance in photo-realistic colorization compared to existing state-of-the-art techniques.
The pyramid dual non-local attention network effectively leverages multi-scale correlations and long-range dependencies for improved color transfer.
The bidirectional fusion strategy ensures semantic consistency and enhances the visual quality of the colorized images.

Conclusions:

The developed exemplar colorization strategy offers a significant advancement in generating visually plausible and semantically consistent colorized images.
The novel network architecture and unsupervised learning approach provide a robust solution for challenging colorization tasks.
This method sets a new benchmark for photo-realistic results in exemplar-based image colorization.