提出多天气图像恢复新方法，自动处理混合天气干扰。
Multi-Weather Image Restoration via Histogram-Based Transformer Feature Enhancement
- 用任务序列生成器与局部块提取图像中的特定天气特征。
- 结合直方图变压器捕捉全局与局部动态范围信息。
- 适合自动驾驶等需应对复杂天气的实际场景使用。
当前恶劣天气下的主流恢复任务主要针对单一天气场景，但现实中多种天气常混合出现且混合程度未知。在此复杂多变的条件下，单一天气恢复模型难以满足实际需求，尤其在自动驾驶等领域亟需能自动处理混合天气并提升图像质量的模型。本文提出任务序列生成模块，配合任务内块，有效提取退化图像中嵌入的任务特异性特征；任务内块引入外部可学习序列，辅助网络捕捉任务相关特征。此外，采用基于直方图的变压器作为网络主干，以捕获全局和局部动态范围特征。所提模型在公开数据集上达到领先性能。
原文摘要 · Abstract (English)
Currently, the mainstream restoration tasks under adverse weather conditions have predominantly focused on single-weather scenarios. However, in reality, multiple weather conditions always coexist and their degree of mixing is usually unknown. Under such complex and diverse weather conditions, single-weather restoration models struggle to meet practical demands. This is particularly critical in fields such as autonomous driving, where there is an urgent need for a model capable of effectively handling mixed weather conditions and enhancing image quality in an automated manner. In this paper, we propose a Task Sequence Generator module that, in conjunction with the Task Intra-patch Block, effectively extracts task-specific features embedded in degraded images. The Task Intra-patch Block introduces an external learnable sequence that aids the network in capturing task-specific information. Additionally, we employ a histogram-based transformer module as the backbone of our network, enabling the capture of both global and local dynamic range features. Our proposed model achieves state-of-the-art performance on public datasets.
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