提出新型降质估计机制，精准建模雨雾图像的复杂退化特征。
MODEM: A Morton-Order Degradation Estimation Mechanism for Adverse Weather Image Recovery
- 基于莫顿码的2D选择性扫描模块，捕捉长程依赖并保持局部结构。
- 双退化估计模块分离全局与局部退化先验，提升恢复精度。
- 适用于多种恶劣天气，适合需要精细退化建模的图像恢复任务。
由于天气引起的伪影具有高度非均匀和空间异质性（如细粒度雨痕与广泛雾霾），恶劣天气图像恢复仍面临重大挑战。准确估计底层退化可为恢复模型提供更精准的指导，实现自适应处理。为此，我们提出面向恶劣天气图像恢复的莫顿码退化估计机制（MODEM）。核心是莫顿码二维选择性扫描模块（MOS2D），融合莫顿编码的空间排序与选择性状态空间模型，以捕获长程依赖并保留局部结构一致性。此外，引入双退化估计模块（DDEM），解耦并估计全局与局部退化先验，动态调节MOS2D模块，实现自适应、上下文感知的恢复。大量实验与消融研究证明，MODEM在多个基准和天气类型下均达到顶尖性能，凸显其对复杂退化动态建模的有效性。代码将发布于 https://github.com/hainuo-wang/MODEM.git。
原文摘要 · Abstract (English)
Restoring images degraded by adverse weather remains a significant challenge due to the highly non-uniform and spatially heterogeneous nature of weather-induced artifacts, e.g., fine-grained rain streaks versus widespread haze. Accurately estimating the underlying degradation can intuitively provide restoration models with more targeted and effective guidance, enabling adaptive processing strategies. To this end, we propose a Morton-Order Degradation Estimation Mechanism (MODEM) for adverse weather image restoration. Central to MODEM is the Morton-Order 2D-Selective-Scan Module (MOS2D), which integrates Morton-coded spatial ordering with selective state-space models to capture long-range dependencies while preserving local structural coherence. Complementing MOS2D, we introduce a Dual Degradation Estimation Module (DDEM) that disentangles and estimates both global and local degradation priors. These priors dynamically condition the MOS2D modules, facilitating adaptive and context-aware restoration. Extensive experiments and ablation studies demonstrate that MODEM achieves state-of-the-art results across multiple benchmarks and weather types, highlighting its effectiveness in modeling complex degradation dynamics. Our code will be released at https://github.com/hainuo-wang/MODEM.git.
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