MCRFS-Net:基于多尺度对比调整和频率选择的单一图像消毒.
Qin Qin1, Lin Shui2, Yanyan Zhang3
1College of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai, 201209, China.
Scientific reports
|July 15, 2025
概括
本研究引入了一种新的方法来改善图像脱雾,通过处理多个尺度和频率的图像来有效处理不均的雾. 这种方法提高了细节的保存和稳定性,以获得更清晰的图像.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 人工智能的人工智能
背景情况:
- 图像脱雾旨在恢复模糊图像的清晰度,但不均的模糊对现有的大气散射模型构成重大挑战.
- 当前的方法经常单独解决图像层面或特征层面的不均性,很少有人能够同时有效解决这两个问题.
研究的目的:
- 开发一种新的图像除雾方法,有效地解决在图像和特征层面上的非均雾.
- 引入一个自适应多尺度频率选择 (AMFS) 模块和一个多尺度对比调节 (MSCR) 损失函数,以提高除雾性能.
主要方法:
- 引入了自适应多尺度频率选择 (AMFS) 模块,包括用于加权特征融合的自适应多尺度模块 (AMSM) 和用于频率域处理的频率选择块 (FSB).
- AMSM集成了多个尺度的功能,以减轻不统一的排气问题,而FSB使用注意力机制来突出重要频率组件并抑制噪音.
- 提出了一个多尺度对比调节 (MSCR) 损失函数,利用跨尺度对比学习来增强特征的一致性.
主要成果:
- 拟议的算法在四个基准数据集上表现出比现有方法更好的性能.
- 在不均的雾条件下实现了更好的细节保存和更好的稳定性.
- 通过整合多尺度特征并将其处理在频率域中,AMFS模块有效地处理了不均的雾.
结论:
- 新型AMFS模块和MSCR损失函数为非统一的图像破坏提供了有效的解决方案.
- 拟议的方法在细节恢复和强度方面提供了显著的改进,超过了当前最先进的技术.
- 这项工作通过解决同时的图像和特征级别不均性,推进了图像消除领域.
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