用小模型实现交通监控雪天图像去雪，效果优于主流方法。
Wavelet-Enhanced Desnowing: A Novel Single Image Restoration Approach for Traffic Surveillance under Adverse Weather Conditions
- 引入双树复小波变换增强特征，精准提取雪区信息。
- 动态卷积加速模块降低计算开销，保持高性能。
- 残差学习模块同时清除雨雪雾遮蔽，适合真实监控场景。
恶劣天气下的图像恢复旨在去除天气粒子引起的退化并提升视觉质量。现有去雾方法多依赖扩大网络规模和增加数据量，计算成本高且特定应用泛化能力不足。在交通监控场景中，主要挑战是去雪和消除雾霾效应。本文提出一种基于小波增强的去雪方法，采用双树复小波变换特征增强模块与动态卷积加速模块，有效处理监控图像中的雪降退化；同时利用残差学习恢复模块消除雨、雪、雾造成的遮蔽效应。该架构能提取并分析雪覆盖区域信息，显著提升去雪性能；残差模块则增强图像清晰度与细节。实验表明，该方法优于部分主流去雪方法，在真实交通监控图像上也表现出良好有效性与准确性。
原文摘要 · Abstract (English)
Image restoration under adverse weather conditions refers to the process of removing degradation caused by weather particles while improving visual quality. Most existing deweathering methods rely on increasing the network scale and data volume to achieve better performance which requires more expensive computing power. Also, many methods lack generalization for specific applications. In the traffic surveillance screener, the main challenges are snow removal and veil effect elimination. In this paper, we propose a wavelet-enhanced snow removal method that use a Dual-Tree Complex Wavelet Transform feature enhancement module and a dynamic convolution acceleration module to address snow degradation in surveillance images. We also use a residual learning restoration module to remove veil effects caused by rain, snow, and fog. The proposed architecture extracts and analyzes information from snow-covered regions, significantly improving snow removal performance. And the residual learning restoration module removes veiling effects in images, enhancing clarity and detail. Experiments show that it performs better than some popular desnowing methods. Our approach also demonstrates effectiveness and accuracy when applied to real traffic surveillance images.
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