MODEM：一种用于恶劣天气图像恢复的莫顿序退化估计机制
MODEM: A Morton-Order Degradation Estimation Mechanism for Adverse Weather Image Recovery
AI总结：
针对恶劣天气图像恢复中退化估计困难的问题，提出基于莫顿序与状态空间模型的MODEM机制，通过双退化估计模块实现自适应恢复，在多个基准上达到最优性能。
摘要：
由于天气引起的伪影具有高度非均匀和空间异质性的特点，例如细粒度的雨条纹与广泛分布的雾霾，恢复被恶劣天气退化的图像仍然是一个重大挑战。准确估计潜在的退化可以直观地为恢复模型提供更有针对性和有效的指导，从而实现自适应处理策略。为此，我们提出了一种用于恶劣天气图像恢复的莫顿序退化估计机制（MODEM）。MODEM的核心是莫顿序二维选择性扫描模块（MOS2D），该模块将莫顿编码的空间排序与选择性状态空间模型相结合，以捕获长距离依赖关系，同时保持局部结构连贯性。作为MOS2D的补充，我们引入了一个双退化估计模块（DDEM），该模块解耦并估计全局和局部退化先验。这些先验动态地调节MOS2D模块，促进自适应和上下文感知的恢复。大量实验和消融研究表明，MODEM在多个基准和天气类型上取得了最先进的结果，突显了其在建模复杂退化动态方面的有效性。我们的代码将在https://github.com/hainuo-wang/MODEM.git发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
