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HLGNet: High-Light Guided Network for low-light instance segmentation with spatial-frequency domain enhancement.

Huaping Zhou¹, Tao Wu², Kelei Sun³

¹School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, 232001, Anhui, China; School of Economics and Management, Anhui University of Science and Technology, Huainan, 232001, Anhui, China; State Key Laboratory for Safe Mining of Deep Coal Resources and Environment Protection, Anhui University of Science and Technology, Huainan, 232001, Anhui, China.

Neural Networks : the Official Journal of the International Neural Network Society

|May 30, 2025

Summary

This summary is machine-generated.

This study introduces HLGNet, a novel network for low-light instance segmentation. HLGNet enhances image features by combining spatial and frequency domain processing, significantly improving performance in challenging lighting conditions.

Keywords:

Dynamic affine transformation Frequency-spatial information High-light image guided Instance segmentation Mutual information

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Area of Science:

Computer Vision
Image Processing
Deep Learning

Background:

Instance segmentation models excel in normal lighting but falter in low-light due to limited detail.
Existing frequency domain methods lack spatial processing, leading to poor boundary delineation and local perception.

Purpose of the Study:

To develop an effective instance segmentation model for low-light conditions.
To improve feature representation by integrating spatial and frequency domain enhancements.

Main Methods:

Proposed HLGNet (High-Light Guided Network) utilizing high-light image masks.
Introduced the Spatial-Frequency Enhancement (SPE) Block for combined local spatial and global frequency information.
Developed the Dynamic Affine Fusion (DAF) module and HLG Decoder for enhanced feature fusion and attention mechanisms.

Main Results:

HLGNet demonstrated superior performance in low-light instance segmentation.
The SPE block effectively integrated spatial and frequency domain features.
The DAF module and HLG Decoder improved capture of detailed targets and global context.

Conclusions:

HLGNet significantly advances low-light instance segmentation capabilities.
The proposed hybrid spatial-frequency approach addresses limitations of prior methods.
The network shows state-of-the-art performance on benchmark datasets.