SOURCE-LINKED INTELLIGENCE

Uncertainty-Guided Adverse Weather Restoration via Gated Transformer Network

arXiv · AI, language, vision and robotics · article · Sep 2, 2026 · UTC

Restoring images degraded by adverse weather remains challenging due to spatially heterogeneous degradations. Many existing weather-specific restoration models rely on weather-agnostic global aggregation, naive cross-scale fusion, and deterministic objectives, which struggle to handle heterogeneous degradations in all-in-one adverse-weather settings. To address these limitations, we propose an Uncertainty-guided Adverse-weather Restoration Network (UAR-Net), a weather-specific AiO framework that integrates a gated transformer with balanced multi-scale skip connections. Specifically, we employ

Read original source ↗ Open in workspace

recordType

paper

region

Global