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Retinex-based low-light image enhancement with multi-channel feature optimization.

Zhiwen Wang¹, Yulong Qiao¹, Yan Cang¹

¹College of Information and Communication Engineering, Harbin Engineering University, Harbin, China.

Science Progress

|April 27, 2026

Summary

This summary is machine-generated.

This study introduces a novel two-stage algorithm for low-light image enhancement, significantly improving brightness, detail, and color fidelity. The method enhances visual quality and boosts object detection performance in challenging conditions.

Keywords:

attention mechanism deep learning feature fusion low-light image enhancement multi-channel decomposition retinex theory

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Area of Science:

Computer Vision
Image Processing
Deep Learning

Background:

Low-light conditions degrade image quality, impacting visual perception and downstream tasks like object detection.
Existing deep learning methods struggle to balance brightness, detail preservation, and color fidelity simultaneously.

Purpose of the Study:

To develop an advanced low-light image enhancement algorithm addressing limitations of current methods.
To improve visual quality and enhance performance in computer vision tasks under low illumination.

Main Methods:

A two-stage Retinex-based algorithm utilizing multi-channel integrated feature optimization.
A three-channel illumination decomposition strategy for independent RGB channel processing to reduce color distortion.
A U-Net-based decomposition network with deformable convolutions, attention mechanisms, and selective-kernel fusion.
A two-branch fusion network for detail enhancement, low-frequency filtering, and curve-based illumination adjustment.

Main Results:

The proposed method outperforms state-of-the-art algorithms in standard image quality metrics (PSNR, SSIM, NIQE).
Demonstrated significant improvement in object detection mean Average Precision (mAP) on the ExDark dataset, increasing by 67.8% (to 0.193).

Conclusions:

The novel algorithm effectively enhances low-light images, achieving superior brightness, detail, and color fidelity.
The enhancement significantly benefits downstream vision tasks, particularly object detection, showcasing practical applicability.