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Double-function enhancement algorithm for low-illumination images based on retinex theory.

Liwei Chen, Yanyan Liu, Guoning Li

Journal of the Optical Society of America. A, Optics, Image Science, and Vision

|February 23, 2023

Summary

This summary is machine-generated.

This study introduces a novel low-illumination image enhancement algorithm that effectively reduces noise and preserves details. The proposed method significantly improves visual quality and naturalness compared to existing techniques.

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Low-illumination images suffer from noise amplification and uneven illumination, hindering detail visibility.
Existing enhancement methods often lead to excessive enhancement or noise, compromising image quality.

Purpose of the Study:

To develop a high-quality image enhancement algorithm for low-illumination conditions.
To address challenges of noise amplification and excessive enhancement in image processing.

Main Methods:

Utilized total-variation model for smoothing V- and S-channel images.
Applied adaptive gamma transform and improved multi-scale Retinex algorithms for V-channel enhancement.
Fused enhanced images based on local intensity amplitude and corrected with a 3D gamma function.

Main Results:

The algorithm significantly reduces Lightness Order Error (LOE) by up to 79.95% compared to classical methods.
Achieved up to 53.43% and 52.13% LOE reduction against state-of-the-art and deep learning algorithms, respectively.
Demonstrated effective noise reduction, detail preservation, and avoidance of over-enhancement.

Conclusions:

The proposed algorithm enhances visual effects in low-illumination images while maintaining naturalness.
It offers a superior alternative for low-illumination image enhancement, outperforming various existing approaches.