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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: Jan 13, 2026

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

Multi-Scale Interactive Network with Color Attention for Low-Light Image Enhancement.

Haoxiang Lu^1,2,3,4, Changna Qian⁴, Ziming Wang⁴

¹Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Sciences, Guangzhou 510080, China.

Sensors (Basel, Switzerland)

|January 10, 2026

Summary

This summary is machine-generated.

We developed MSINet, a novel multi-scale interactive network, to enhance low-light images by balancing light and color correction. This method effectively explores local and global image features for superior low-light image enhancement (LLIE) results.

Keywords:

color attention mechanism low-light image enhancement multi-scale feature transformer

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Last Updated: Jan 13, 2026

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Published on: November 14, 2011

Area of Science:

Computer Vision
Image Processing

Background:

Low-light image enhancement (LLIE) is vital for computer vision.
Existing methods often fail to balance light enhancement and color correction.
Images contain multi-level information crucial for effective enhancement.

Purpose of the Study:

To propose a novel network, MSINet, for effective low-light image enhancement.
To address the challenge of balancing light enhancement and color correction.
To leverage multi-scale features for improved LLIE.

Main Methods:

Proposed MSINet, a multi-scale interactive network with color attention.
Employed a CNN-based branch with residual channel attention blocks (RCABs) for local features.
Utilized a Transformer-based branch with cross-scale attention (CSA) and multi-head self-attention (MHSA) for global features.
Integrated a color correction branch (CCB) with self-attention (SA) for color fidelity.

Main Results:

MSINet effectively explores local and global image features through interaction modules.
The CCB ensures color fidelity in the enhanced images.
Extensive experiments show MSINet outperforms state-of-the-art LLIE methods.

Conclusions:

MSINet achieves superior performance in both light enhancement and color correction for low-light images.
The proposed architecture effectively integrates multi-scale feature extraction and interaction.
MSINet offers a promising solution for challenging low-light image enhancement tasks.