Multi-Weather Image Restoration via Histogram-Based Transformer Feature Enhancement
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| Format: | Preprint |
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2024
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| author | Wen, Yang Lai, Anyu Qian, Bo Wang, Hao Shi, Wuzhen Cao, Wenming |
| author_facet | Wen, Yang Lai, Anyu Qian, Bo Wang, Hao Shi, Wuzhen Cao, Wenming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_06334 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Weather Image Restoration via Histogram-Based Transformer Feature Enhancement Wen, Yang Lai, Anyu Qian, Bo Wang, Hao Shi, Wuzhen Cao, Wenming Computer Vision and Pattern Recognition 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. |
| title | Multi-Weather Image Restoration via Histogram-Based Transformer Feature Enhancement |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.06334 |