Adverse weather image restoration aims to remove unwanted degraded artifacts, such as haze, rain, and snow, caused by adverse weather conditions. Existing methods achieve remarkable results for addressing single-weather conditions. However, they face challenges when encountering unpredictable weather conditions, which often happen in real-world scenarios. Although different weather conditions exhibit different degradation patterns, they share common characteristics that are highly related and complementary, such as occlusions caused by degradation patterns, color distortion, and contrast attenuation due to the scattering of atmospheric particles. Therefore, we focus on leveraging common knowledge across multiple weather conditions to restore images in a unified manner. In this paper, we propose a Triplet Attention Network (TANet) to efficiently and effectively address all-in-one adverse weather image restoration. TANet consists of Triplet Attention Block (TAB) that incorporates three types of attention mechanisms: Local Pixel-wise Attention (LPA) and Global Strip-wise Attention (GSA) to address occlusions caused by non-uniform degradation patterns, and Global Distribution Attention (GDA) to address color distortion and contrast attenuation caused by atmospheric phenomena. By leveraging common knowledge shared across different weather conditions, TANet successfully addresses multiple weather conditions in a unified manner. Experimental results show that TANet efficiently and effectively achieves state-of-the-art performance in all-in-one adverse weather image restoration. The source code is available at https://github.com/xhuachris/TANet-ACCV-2024.

翻译：恶劣天气图像复原旨在消除由恶劣天气条件引起的雾霾、雨雪等有害退化伪影。现有方法在处理单一天气条件时取得了显著成果，但在面对现实场景中常见的不可预测天气条件时仍面临挑战。尽管不同天气条件呈现不同的退化模式，但它们共享高度相关且互补的共同特征，例如退化模式造成的遮挡、颜色失真以及大气粒子散射导致的对比度衰减。因此，我们致力于利用跨多种天气条件的共同知识，以统一方式实现图像复原。本文提出三元注意力网络（TANet），用于高效且有效地解决一体化恶劣天气图像复原问题。TANet包含三元注意力模块（TAB），该模块整合了三种注意力机制：局部像素级注意力（LPA）与全局条带级注意力（GSA）用于处理非均匀退化模式造成的遮挡，全局分布注意力（GDA）则用于处理大气现象引起的颜色失真和对比度衰减。通过利用不同天气条件间共享的共同知识，TANet成功以统一方式处理多种天气条件。实验结果表明，TANet在一体化恶劣天气图像复原任务中高效且有效地实现了最先进的性能。源代码公开于：https://github.com/xhuachris/TANet-ACCV-2024。