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Illumination-aware divide-and-conquer network for improperly-exposed image enhancement.

Fenggang Han¹, Kan Chang¹, Guiqing Li²

¹School of Computer and Electronic Information, Guangxi University, Nanning 530004, China.

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

|September 18, 2024

Summary

This study introduces the illumination-aware divide-and-conquer network (IDNet) for consistent image enhancement across various exposures. IDNet effectively restores details and improves illumination by decomposing images and using illumination-aware features.

Keywords:

Convolutional neural network Cross attention Discrete wavelet transform Exposure correction Image enhancement

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Area of Science:

Computer Vision
Image Processing
Deep Learning

Background:

Improperly-exposed images suffer from poor illumination, low contrast, and loss of detail.
Existing enhancement methods struggle with consistency across different exposure conditions.
A robust solution is needed for consistent and detailed image enhancement.

Purpose of the Study:

To propose an illumination-aware divide-and-conquer network (IDNet) for consistent image enhancement.
To address the challenge of learning complex mappings for diverse exposure conditions.
To restore rich details and improve visual quality in images.

Main Methods:

Utilized discrete wavelet transform (DWT) to decompose images into low-frequency (LF) and high-frequency (HF) components.
Extracted illumination-related global information to modulate dynamic convolution weights for LF component correction.
Integrated knowledge from the corrected LF component into the HF component to restore fine-scale details.

Main Results:

The proposed IDNet achieved superior performance compared to state-of-the-art methods.
Demonstrated consistent enhancement results across multiple exposure conditions.
Successfully restored fine-scale structures and details in images.

Conclusions:

IDNet provides a robust and effective solution for image enhancement under varying exposure levels.
The illumination-aware approach ensures consistent results and preserves image details.
The method shows significant improvements on datasets with multiple exposures.