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MEF-CAAN: Multi-Exposure Image Fusion Based on a Low-Resolution Context Aggregation Attention Network.

Wenxiang Zhang¹, Chunmeng Wang¹, Jun Zhu¹

¹School of Computer Engineering, Jinling Institute of Technology, Nanjing 211169, China.

Sensors (Basel, Switzerland)

|April 26, 2025

Summary

This study introduces a new deep learning method for multi-exposure image fusion, improving detail recovery in extreme exposures using a low-resolution context aggregation attention network (MEF-CAAN). The unsupervised network enhances feature extraction for superior fused image quality.

Keywords:

context aggregation attention network guided filtering for upsampling multi-exposure image fusion multi-resolution

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Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Deep learning methods are prevalent for multi-exposure image fusion.
Existing methods struggle with feature extraction in extremely exposed image regions.

Purpose of the Study:

To propose an effective multi-exposure image fusion method addressing limitations in detail recovery.
To introduce the low-resolution context aggregation attention network (MEF-CAAN) for enhanced feature extraction.

Main Methods:

Utilizing a low-resolution context aggregation attention network (CAAN) to predict low-resolution weight maps.
Employing guided filtering for upsampling (GFU) to generate high-resolution weight maps.
Generating the final fused image via weighted summation of high-resolution inputs.

Main Results:

The proposed unsupervised network adaptively adjusts channel weights for improved feature extraction.
Quantitative and qualitative evaluations demonstrate superior performance compared to state-of-the-art methods.
Enhanced recovery of information and details in extremely exposed areas of fused images.

Conclusions:

The MEF-CAAN method offers significant improvements in multi-exposure image fusion.
The network's unsupervised nature and adaptive channel weighting contribute to its effectiveness.
This approach advances the state-of-the-art in image fusion techniques.