[1]WANG Weiping,DIAO Yapeng.Optimized multi-exposure image fusion algorithm integrating pyramid and multi-scale attention[J].CAAI Transactions on Intelligent Systems,2025,20(4):916-927.[doi:10.11992/tis.202406032]
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Optimized multi-exposure image fusion algorithm integrating pyramid and multi-scale attention

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 4 Page number: 916-927 Column: 学术论文—机器学习 Public date: 2025-08-05
Title:
Optimized multi-exposure image fusion algorithm integrating pyramid and multi-scale attention
Author(s):
WANG Weiping;  DIAO Yapeng
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Keywords:
image fusion; attention mechanism; pyramid network; multi-exposure; detail extraction; color correction; multi-scale; deep learning
CLC:
TP181
DOI:
10.11992/tis.202406032
Abstract:
The article introduces a multi-exposure image fusion technique named DPEPA-MEF (deep pyramid exposure pyramid attention-multi-exposure fusion). This technique aims to address image noise, blur, and detail loss caused by complex lighting conditions in real scenes. The DPEPA-MEF method effectively combines images with varying exposure levels to solve issues such as high contrast, low light, and color and brightness balance. It consists of three modules, which improve upon deep pyramid exposure (DPE). The first module focuses on content detail extraction, the second on color mapping and correction, and the third on image recovery using a multi-scale feature pyramid. Experimental results indicate that under different lighting conditions and dynamic scenes, DPEPA-MEF can more effectively fuse multiple exposure images. The resulting images exhibit excellent detail preservation, color reproduction, and contrast. Both quantitative evaluation metrics and subjective visual assessments demonstrate the significant advantages of DPEPA-MEF, confirming its great potential and superiority in practical applications.
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