MEF-CAAN:基于低分辨率的背景聚合注意力网络的多曝光图像融合
Wenxiang Zhang1, Chunmeng Wang1, Jun Zhu1
1School of Computer Engineering, Jinling Institute of Technology, Nanjing 211169, China.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
本研究引入了一种用于多曝光图像融合的新深度学习方法,通过使用低分辨率上下文聚合注意力网络 (MEF-CAAN) 改善极端曝光中的细节恢复. 无监督网络增强了特征提取,以获得卓越的融合图像质量.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 对于多曝光图像融合,深度学习方法很普遍.
- 现有的方法在极度曝光的图像区域中难以提取特征.
研究的目的:
- 提出一种有效的多曝光图像融合方法,解决细节恢复方面的局限性.
- 引入低分辨率上下文聚合注意网络 (MEF-CAAN) 进行增强的特征提取.
主要方法:
- 使用低分辨率的上下文聚合注意网络 (CAAN) 来预测低分辨率的重量图.
- 采用导向过用于上采样 (GFU) 来生成高分辨率的重量图.
- 通过高分辨率输入的加权总和生成最终的融合图像.
主要成果:
- 拟议的无监督网络可自适应地调整通道重量,以改善特征提取.
- 定量和定性评估表明,与最先进的方法相比,性能优越.
- 在融合图像的极度曝光区域中,增强了信息和细节的恢复.
结论:
- MEF-CAAN方法在多次曝光图像融合中提供了显著的改进.
- 网络的无监督性和适应性道权重有助于其有效性.
- 这种方法推进了图像融合技术的最先进技术.
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