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Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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Updated: May 3, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Low-light image enhancement using generative adversarial networks.

Litian Wang¹, Liquan Zhao², Tie Zhong¹

¹Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology(Ministry of Education), Northeast Electric Power University, Jilin, 132012, China.

Scientific Reports

|August 9, 2024

Summary

This summary is machine-generated.

This study introduces a generative adversarial network to enhance low-light images. The method improves brightness, color, and detail, outperforming existing techniques on both synthetic and real-world low-light images.

Keywords:

Generative adversarial networks Illumination attention module Multi-scale feature extraction module Nighttime road scene image enhancement

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Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Low-light conditions reduce image brightness, obscuring details and hindering image analysis.
Existing low-light image enhancement methods struggle to restore both color and detail effectively.

Purpose of the Study:

To develop an advanced generative adversarial network (GAN) for superior low-light image enhancement.
To improve the brightness, color fidelity, and detail preservation of images captured in low-light environments.

Main Methods:

A generative network incorporating multi-scale feature extraction and an illumination attention module.
An encoder-decoder architecture within the generative network for enhanced feature processing.
A dual-discriminator adversarial network (global and local) to refine image generation.
An improved loss function combining color and perceptual losses to minimize color distortion.

Main Results:

The proposed GAN achieved results closer to normally illuminated images for synthetic low-light data.
Enhanced real low-light images demonstrated superior detail retention and clarity compared to other methods.
Quantitative performance metrics indicated higher overall effectiveness for the proposed enhancement technique.

Conclusions:

The developed generative adversarial network offers significant improvements in low-light image enhancement.
The method effectively addresses challenges in brightness, color, and detail restoration.
It shows strong performance on both synthesized and real-world low-light image datasets.