Abstract
Flare caused by unintended light scattering or reflection in night scenes significantly degrades image quality. Existing methods explore frequency factors and semantic priors but fail to comprehensively integrate all relevant information. To address this, we propose LUFormer, a luminance-informed Transformer network with localized frequency augmentation. Central to our approach are two key modules: the luminance-guided branch (LGB) and the dual domain hybrid attention (DDHA) unit. The LGB provides global brightness semantic priors, emphasizing the disruption of luminance distribution caused by flare. The DDHA improves deep flare representation in both the spatial and frequency domains. In the spatial domain, it broadens the receptive field through pixel rearrangement and cross-window dilation, while in the frequency domain, it emphasizes and amplifies low-frequency components via a compound attention mechanism. Our approach leverages the LGB, which globally guides semantic refinement, to construct a U-shaped progressive focusing framework. In this architecture, the DDHA locally augments multi-domain features across multiple scales. Extensive experiments on real-world benchmarks demonstrate that the proposed LUFormer outperforms state-of-the-art methods. The code is publicly available at: https://github.com/HeZhao0725/LUFormer.
