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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: Mar 1, 2026

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

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Quaternion Locality-Constrained Coding for Color Face Hallucination.

Licheng Liu, Shutao Li, C L Philip Chen

IEEE Transactions on Cybernetics

|May 26, 2017

Summary

This summary is machine-generated.

This study introduces a novel quaternion-based locality-constrained coding (QLC) model for enhanced color face hallucination. QLC effectively preserves inter-channel correlations, outperforming existing methods.

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Area of Science:

Computer Vision
Image Processing
Quaternion Algebra

Background:

Conventional locality linear coding (LLC) is limited to grayscale images.
Existing color image methods often ignore correlations between color channels.
This leads to suboptimal performance in tasks like face hallucination.

Purpose of the Study:

To propose a novel quaternion-based locality-constrained coding (QLC) model.
To address the limitations of conventional LLC for color images.
To improve color face hallucination by preserving inter-channel correlations.

Main Methods:

Representing color face images as quaternion matrices.
Transforming channel images into an orthogonal feature space.
Encoding coefficients within the quaternion domain using QLC.

Main Results:

The QLC model successfully preserves inherent correlations among color channels.
QLC exposes the true topology of the image patch manifold.
Experimental results show superior performance in color face hallucination.

Conclusions:

The proposed QLC model offers a significant advancement for color face hallucination.
QLC leverages quaternion algebra and locality coding for improved image representation.
This method demonstrates superior performance compared to state-of-the-art techniques.