基于VMT-Net的恶劣天气图像恢复方法
Zhongmin Liu1, Xuewen Yu1, Wenjin Hu2
1School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China.
Journal of imaging
|November 26, 2025
概括
本研究介绍了VMT-Net,这是一个使用Vision Mamba和Transformer架构的新型图像恢复网络,用于在恶劣天气中增强细节恢复. 该网络有效地减轻语义损失和模糊,改善图像质量.
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:
- 像雪和雨这样的恶劣天气条件会降低图像质量,导致全球语义损失和局部细节模糊.
- 现有的图像修复方法在具有挑战性的环境中与空间语义冲突和信息丢失作斗争.
研究的目的:
- 开发一个先进的图像恢复网络,能够解决语义丢失,细节模糊和空间语义冲突.
- 为了提高在各种恶劣天气条件下拍摄的图像的重建质量.
主要方法:
- 提出了一个Vision-Mamba-Transformer (VMT) 模块,将Vision Mamba的远程依赖模型与Transformer的全球上下文推理集成在一起.
- 引入了一个自适应内容指导 (ACG) 模块,用于使用动态门和空间通道注意力来有效地实现层间特征融合.
- 在U-Net骨干中嵌入VMT和ACG模块,用于多级特征建模和跨层融合.
主要成果:
- 在Snow100K数据集上,VMT-Net取得了显著的改进,PSNR增幅高达0.89 dB,SSIM增幅为0.91%.
- 证明了与Outdoor-Rain和Raindrop数据集的基线可比的性能,具有卓越的现实世界细节恢复.
- 拟议的网络在各种恶劣天气场景中表现出强大的稳定性和强大的细节恢复能力.
结论:
- 在VMT-Net中整合Mamba和Transformer架构有效地提高了在恶劣天气下图像恢复的性能.
- VMT和ACG模块有助于减轻信息丢失,减少细节模糊,并改善语义一致性.
- 在具有挑战性的环境条件下,VMT-Net提供了一个强大的解决方案,用于高质量的图像重建.
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