Multi-Weather Image Restoration via Histogram-Based Transformer Feature Enhancement
<|reference_start|>Multi-Weather Image Restoration via Histogram-Based Transformer Feature Enhancement: 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.<|reference_end|>
@article{wen2024multi-weather,
    title={Multi-Weather Image Restoration via Histogram-Based Transformer Feature
  Enhancement},
    author={Yang Wen, Anyu Lai, Bo Qian, Hao Wang, Wuzhen Shi and Wenming Cao},
    journal={arXiv preprint arXiv:2409.06334},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06334},
    primaryClass={cs.CV}
}