Multi-Weather Image Restoration via Histogram-Based Transformer Feature Enhancement

• URL: http://arxiv.org/abs/2409.06334v1
• Date: Tue, 10 Sep 2024 08:47:03 GMT
• Title: Multi-Weather Image Restoration via Histogram-Based Transformer Feature Enhancement
• Authors: Yang Wen, Anyu Lai, Bo Qian, Hao Wang, Wuzhen Shi, Wenming Cao,
• Abstract summary: In adverse weather conditions, single-weather restoration models struggle to meet practical demands. There is an urgent need for a model capable of effectively handling mixed weather conditions and enhancing image quality in an automated manner. 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.
• Score: 14.986500375481546
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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