统一建模多种恶劣天气的物理先验，提升图像恢复效果
Unifying Physically-Informed Weather Priors in A Single Model for Image Restoration Across Multiple Adverse Weather Conditions
- 构建统一成像模型，融合粒子遮挡与雾状散射效应
- 在雨、雾等多场景下优于当前最佳方法
- 适合需要跨天气图像修复的应用开发者
针对多种恶劣天气下的图像恢复问题，本文提出一种统一建模方法，通过分析不同天气条件下的共同视觉特征，建立考虑个体可见粒子与雾状聚集散射效应的统一成像模型。设计基于天气先验的新型网络结构，利用估计的遮挡和透射率信息增强特征表示，实现对近处雨滴和远处雾气影响的协同恢复。在多种恶劣天气场景下的实验表明，该方法在客观指标与主观视觉质量上均显著优于现有先进方法。
原文摘要 · Abstract (English)
Image restoration under multiple adverse weather conditions aims to develop a single model to recover the underlying scene with high visibility. Weather-related artifacts vary with the particle's distance to the camera according to the established scene visibility analysis, where close and faraway regions are more affected by falling drops and fog effects, respectively. Existing methods fail to consider this weather-specific physical visual process; thus, the restoration performance is limited. In this work, we analyze the common visual factors in adverse weather conditions and present a unified imaging model that considers the individually visible particles and fog-like aggregate scattering effects. Further, we design a novel weather-prior-based network, which leverages the weather-related prior information to help recover the scene by enhancing the features using the estimated occlusion and transmission. Experimental results in multiple adverse scenarios show the superiority of our method against state-of-the-art methods.
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