基于门控Transformer网络的不确定性引导恶劣天气图像恢复

中文摘要 AI 辅助

由于空间异质性退化，恢复受恶劣天气影响的图像仍具挑战性。现有多数天气专用恢复模型依赖与天气无关的全局聚合、朴素跨尺度融合及确定性目标，难以处理全场景恶劣天气设置中的异质性退化。为解决这些局限，我们提出不确定性引导恶劣天气恢复网络（UAR-Net），这是一种天气专用的全在一（AiO）框架，集成门控Transformer与平衡多尺度跳跃连接。具体而言，我们采用门控双尺度Transformer块（GDTB）联合建模选择性全局交互与多尺度局部结构，使用渐进式平衡多尺度跳跃连接（BMSC）实现平衡多尺度特征融合，以及不确定性感知细化头（URH），用于伪影去除、细节增强和预测不确定性估计。该模型由亮度感知能量损失（BAE-Loss）监督，以鼓励准确重建并获得校准良好的不确定性。大量实验表明，我们的方法在多个恶劣天气基准上达到了最先进的性能，代码将在接收后开源。

英文摘要

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 Gated Dual-scale Transformer Blocks (GDTB) to jointly model selective global interactions and multi-scale local structures, a progressive Balanced Multi-scale Skip Connection (BMSC) for balanced multi-scale feature integration, and an Uncertainty-Aware Refinement Head (URH) that performs artifact removal, detail enhancement, and predictive uncertainty estimation. The model is supervised by a Brightness-Aware Energy Loss (BAE-Loss) to encourage accurate reconstruction with well-calibrated uncertainty. Extensive experiments demonstrate that our method achieves state-of-the-art performance across multiple adverse-weather benchmarks. The codes will open source upon acceptance.

发表机构

• School of Automation, Southeast University（东南大学自动化学院）
• Chair of Robotics, Artificial Intelligence and Real-time Systems, Technical University of Munich（慕尼黑工业大学机器人、人工智能与实时系统讲席教授组）

机构由 AI 辅助整理，请以论文原文为准。