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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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Updated: Aug 24, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Colorizing the Past: Deep Learning for the Automatic Colorization of Historical Aerial Images.

Elisa Mariarosaria Farella¹, Salim Malek¹, Fabio Remondino¹

¹3D Optical Metrology (3DOM) Unit, Fondazione Bruno Kessler (FBK), Via Sommarive 18, 38123 Trento, Italy.

Journal of Imaging

|October 26, 2022

Summary

This summary is machine-generated.

Deep learning automates grayscale image colorization. A new Hyper-U-NET neural network effectively colors historical aerial images, advancing image processing for various applications.

Keywords:

aerial images deep learning grayscale image colorization historical photos

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Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Deep learning advancements enable automated grayscale image colorization.
Image colorization is valuable for media, medical, geospatial, and historical photograph restoration.

Purpose of the Study:

To introduce a novel neural network architecture for historical aerial image colorization.
To address limitations in existing fully automatic image colorization methods.

Main Methods:

Developed Hyper-U-NET, a neural network combining U-NET architecture with HyperConnections.
Utilized a training dataset of approximately 10,000 colored aerial image patches.

Main Results:

The Hyper-U-NET architecture demonstrates effectiveness in colorizing historical black and white aerial images.
The study contributes a novel approach to automated image colorization.

Conclusions:

Hyper-U-NET offers a promising solution for historical aerial image colorization.
The developed neural network and dataset are publicly available to encourage further research.