打破天气-内容耦合：类型-严重度引导的渐进解耦用于全合一红外图像恢复
Breaking Weather-Content Coupling: Type-Severity Guided Progressive Disentanglement for All-in-One Infrared Restoration
AI总结：
针对红外图像在恶劣天气下难以区分真实热结构与天气虚假响应的问题，提出TSGPD-IR网络，通过类型-严重度引导的渐进解耦实现全合一恢复，减少伪影与过度抑制。
AI中文摘要：
红外（IR）成像对于自动驾驶、遥感及其他感知任务至关重要。然而，恶劣天气可能引入与真实热结构纠缠的虚假结构响应。现有的红外恢复方法通常针对单一退化类型设计，或直接从退化纠缠的表示中进行重建。因此，它们难以区分内在热结构与天气引起的虚假响应，也难以适应空间变化的退化严重度，导致伪影或过度抑制微弱但有意义的热响应。为解决这些问题，我们提出了TSGPD-IR，一种类型-严重度引导的渐进解耦网络，用于全合一红外恢复，将恢复引导分解为任务级天气语义和区域级退化严重度。具体而言，天气与语义协同引导的多级提示生成模块结合全局天气语义与阶段级局部特征，生成自适应提示，逐步抑制退化引起的响应，同时保留内在热结构。为用空间恢复控制补充全局天气语义，代理监督的区域退化估计器无需人工标注即可推导严重度监督，并预测空间变化的退化先验。在这些线索的引导下，多源协作专家选择策略使用共享分支保留天气不变的热结构，并通过分层路由选择天气特定专家池和严重度兼容的区域专家。该设计逐步将退化干扰与真实热内容分离，实现区域自适应恢复，减少残余伪影和过度抑制。
英文摘要：
Infrared (IR) imaging is crucial for autonomous driving, remote sensing, and other perception tasks. However, adverse weather may introduce fake structural responses that are entangled with real thermal structures. Existing IR restoration methods are typically designed for a single degradation type or directly reconstruct from degradation-entangled representations. Consequently, they struggle to distinguish intrinsic thermal structures from weather-induced fake responses and to accommodate spatially varying degradation severity, leading to artifacts or the over-suppression of weak but meaningful thermal responses. To address these issues, we propose TSGPD-IR, a type-severity guided progressive disentanglement network for all-in-one infrared restoration that factorizes restoration guidance into task-level weather semantics and region-level degradation severity. Specifically, a Weather and Semantic Co-Guided Multi-Level Prompt Generation Module combines global weather semantics with stage-wise local features to generate adaptive prompts that progressively suppress degradation-induced responses while preserving intrinsic thermal structures. To complement global weather semantics with spatial restoration control, a Proxy-Supervised Regional Degradation Estimator derives severity supervision without manual annotations and predicts spatially varying degradation priors. Guided by these cues, a Multi-Source Collaborative Expert Selection Strategy uses a shared branch to preserve weather-invariant thermal structures and hierarchical routing to select weather-specific expert pools and severity-compatible regional experts. This design progressively separates degradation interference from genuine thermal content and enables region-adaptive restoration, reducing both residual artifacts and over-suppression.
