MODEM: A Morton-Order Degradation Estimation Mechanism for Adverse Weather Image Recovery

Hainuo Wang, Qiming Hu, Xiaojie Guo

Keywords: Degradation Estimation, Adverse Weather, Image Restoration

TL;DR: MODEM tackles tricky weather image restoration by estimating degradation (DDEM) and using it to guide a novel Morton-order state-space model (MOS2D) for adaptive, context-aware cleaning.

Abstract: Restoring images degraded by adverse weather remains a significant challenge due to the highly non-uniform and spatially heterogeneous nature of weather-induced artifacts, \emph{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 soon.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 2310