从失败中学习：在半监督真实世界恶劣天气去除中利用不可靠预测

中文摘要 AI 辅助

恶劣天气图像恢复旨在恢复被雨、雾、雪及其他天气引起的伪影所退化的图像，从而提高户外视觉系统的鲁棒性。现有的统一恢复模型由于依赖合成监督和语义约束不足，对真实世界场景的泛化能力有限。本文提出了一种新颖的师生半监督框架，以同时解决这两个挑战。具体而言，我们引入了一个不可靠数据库，将失败的教师预测保留为对比学习的信息性负样本，而可靠数据库则存储高质量的教师预测作为正样本。通过联合利用可靠的伪地面真值和不可靠的教师输出，所提出的框架学习增强期望的恢复特征，同时避免常见失败。我们进一步提出了一种基于相位谱的语义约束，以高效且自然对齐的语义先验替代计算昂贵的基于文本的监督。还设计了一种自适应相位一致性损失，根据退化严重程度动态平衡退化输入与教师伪地面真值之间的监督。在真实世界基准上的大量实验表明，所提方法在恢复质量和感知保真度方面持续优于现有最先进方法，并展现出对真实世界恶劣天气条件更强的泛化能力。

英文摘要

Adverse weather image restoration aims to recover images degraded by rain, haze, snow, and other weather-induced artifacts, thereby improving the robustness of outdoor vision systems. Existing unified restoration models exhibit limited generalization to real-world scenes due to their reliance on synthetic supervision and insufficient semantic constraints. In this paper, we propose a novel student--teacher semi-supervised framework that addresses both challenges. Specifically, we introduce an unreliable database that preserves failed teacher predictions as informative negative samples for contrastive learning, while a reliable database stores high-quality teacher predictions as positive samples. By jointly exploiting reliable pseudo-ground truths and unreliable teacher outputs, the proposed framework learns to enhance desirable restoration characteristics while avoiding common failures. We further propose a phase spectrum-based semantic constraint that replaces computationally expensive text-based supervision with an efficient and naturally aligned semantic prior. An adaptive phase consistency loss is also designed to dynamically balance supervision between the degraded input and teacher pseudo-ground truths according to degradation severity. Extensive experiments on real-world benchmarks demonstrate that the proposed method consistently outperforms existing state-of-the-art approaches in restoration quality and perceptual fidelity while exhibiting stronger generalization to real-world adverse weather conditions.

发表机构

• College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
• Nanyang Technological University（南洋理工大学）

机构由 AI 辅助整理，请以论文原文为准。