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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: Jul 31, 2025

Qualitative Identification of Carboxylic Acids, Boronic Acids, and Amines Using Cruciform Fluorophores

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Feature ghost imaging for color identification.

Zihan Gao, Minghui Li, Peixia Zheng

|May 9, 2023

Summary

This summary is machine-generated.

Feature ghost imaging (FGI) converts color information into edge features for simultaneous shape and color object imaging. This new technique enhances computational ghost imaging (CGI) capabilities with a single-pixel detector.

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Last Updated: Jul 31, 2025

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Area of Science:

Optics and Photonics
Image Processing
Computational Imaging

Background:

Traditional computational ghost imaging (CGI) primarily retrieves object intensity information.
Imaging colored objects often requires complex setups or multiple detection rounds.
Extracting both shape and color information simultaneously presents a significant challenge in optical imaging.

Purpose of the Study:

To introduce a novel imaging technique, feature ghost imaging (FGI), based on CGI.
To enable simultaneous acquisition of object shape and color information in a single detection round.
To extend the capabilities of CGI for imaging colored objects.

Main Methods:

Developed FGI by converting object color information into distinguishable edge features.
Utilized different order operators to extract these edge features from retrieved grayscale images.
Employed a single-pixel detector for data acquisition.
Validated the technique through numerical simulations and experimental verification.

Main Results:

Demonstrated the ability of FGI to retrieve both shape and color information of objects simultaneously.
Successfully presented feature distinction for rainbow colors.
Experimental results verified the practical performance and feasibility of FGI.

Conclusions:

FGI offers a new approach for imaging colored objects by leveraging edge feature extraction.
The technique successfully extends the functionality of CGI, allowing for simultaneous shape and color imaging.
FGI maintains the experimental simplicity of traditional CGI while enhancing its application scope.