SLCFormer: Spectral-Local Context Transformer with Physics-Grounded Flare Synthesis for Nighttime Flare Removal
Xiyu Zhu, Wei Wang, Xin Yuan, Xiao Wang

TL;DR
SLCFormer is a spectral-local context transformer that effectively removes complex nighttime lens flares by combining frequency domain modeling, spatial enhancement, and physics-based flare synthesis, achieving state-of-the-art results.
Contribution
The paper introduces SLCFormer, a novel framework integrating frequency and spatial modules for flare removal, and a physics-grounded flare synthesis pipeline for training.
Findings
Outperforms existing methods on Flare7K++ dataset
Achieves superior quantitative metrics and visual quality
Generalizes well to real nighttime flare scenarios
Abstract
Lens flare is a common nighttime artifact caused by strong light sources scattering within camera lenses, leading to hazy streaks, halos, and glare that degrade visual quality. However, existing methods usually fail to effectively address nonuniform scattered flares, which severely reduces their applicability to complex real-world scenarios with diverse lighting conditions. To address this issue, we propose SLCFormer, a novel spectral-local context transformer framework for effective nighttime lens flare removal. SLCFormer integrates two key modules: the Frequency Fourier and Excitation Module (FFEM), which captures efficient global contextual representations in the frequency domain to model flare characteristics, and the Directionally-Enhanced Spatial Module (DESM) for local structural enhancement and directional features in the spatial domain for precise flare removal. Furthermore, we…
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Taxonomy
TopicsImage Enhancement Techniques · Impact of Light on Environment and Health · Computer Graphics and Visualization Techniques
