A Multi-Scale Large Kernel Attention Network for Single Image Deraining

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Abstract
Single image deraining aims to recover clean background scenes from raincontaminated images. Existing methods often enlarge the receptive field tomodel long-range spatial dependencies and better capture rain streak patterns. However, many of them rely on Transformer-based self-attention orconventional large-kernel convolutions, whose computational and parametercosts grow rapidly with the receptive field, limiting efficiency and practicality. To address this issue, we propose a Multi-Scale Large Kernel AttentionNetwork for efficient and high-quality single image deraining. Specifically,we design a Large Kernel Attention Block that combines strip convolutionsand dilated convolutions within an attention framework, enabling near-globalcontext aggregation with low computational overhead. To alleviate detaildistortion caused by long-range modeling, we further introduce a Multi-scaleProgressive Detail Recovery module, which progressively fuses cross-scalefeatures and suppresses residual degradation for fine-detail restoration. Inaddition, an Edge Recovery Block is incorporated to enhance horizontal andvertical structural cues, improving edge consistency and overall structuralfidelity. Extensive experiments on both synthetic and real-world benchmarkdatasets demonstrate that MLKANet achieves competitive deraining performance while maintaining favorable efficiency.

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