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Related Experiment Video

Updated: Oct 9, 2025

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

Low-Light Image Enhancement Based on Generative Adversarial Network.

Nandhini Abirami R¹, Durai Raj Vincent P M¹

¹School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.

Frontiers in Genetics

|December 16, 2021

Summary

This study introduces a novel low-light image enhancement technique (LIMET) using a conditional generative adversarial network. The method significantly improves image quality in low-light conditions, outperforming existing approaches.

Keywords:

computer vision convolutional neural network deep learning facial expression recognition generative adversarial network human-robot interaction

Related Experiment Videos

Last Updated: Oct 9, 2025

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

Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Low-light image capture degrades visibility, impacting vision-based algorithms.
Existing deep learning methods for low-light enhancement lack effective network structures, yielding unsatisfactory results.

Purpose of the Study:

To present a novel low-light image enhancement technique (LIMET) for improved image quality.
To address the limitations of current methods by proposing a fine-tuned conditional generative adversarial network.

Main Methods:

Developed a low-light image enhancement technique (LIMET) utilizing a conditional generative adversarial network.
Employed a dual-discriminator architecture to ensure realistic and natural enhanced image outputs.
Evaluated the approach on benchmark datasets including LIME, DICM, and MEF.

Main Results:

The proposed LIMET approach achieved state-of-the-art performance compared to existing methods.
Achieved high Visual Information Fidelity (VIF) scores: 0.709123 (LIME), 0.849982 (DICM), and 0.619342 (MEF).
Demonstrated superior image quality assessment over degraded inputs.

Conclusions:

The proposed LIMET method effectively enhances low-light images, offering a significant improvement over prior techniques.
The dual-discriminator conditional generative adversarial network architecture is crucial for generating realistic and high-fidelity enhanced images.