Artificial lights commonly leave strong lens flare artifacts on the images captured at night, degrading both the visual quality and performance of vision algorithms. Existing flare removal approaches mainly focus on removing daytime flares and fail in nighttime cases. Nighttime flare removal is challenging due to the unique luminance and spectrum of artificial lights, as well as the diverse patterns and image degradation of the flares. The scarcity of the nighttime flare removal dataset constraints the research on this crucial task. In this paper, we introduce Flare7K++, the first comprehensive nighttime flare removal dataset, consisting of 962 real-captured flare images (Flare-R) and 7,000 synthetic flares (Flare7K). Compared to Flare7K, Flare7K++ is particularly effective in eliminating complicated degradation around the light source, which is intractable by using synthetic flares alone. Besides, the previous flare removal pipeline relies on the manual threshold and blur kernel settings to extract light sources, which may fail when the light sources are tiny or not overexposed. To address this issue, we additionally provide the annotations of light sources in Flare7K++ and propose a new end-to-end pipeline to preserve the light source while removing lens flares. Our dataset and pipeline offer a valuable foundation and benchmark for future investigations into nighttime flare removal studies. Extensive experiments demonstrate that Flare7K++ supplements the diversity of existing flare datasets and pushes the frontier of nighttime flare removal towards real-world scenarios.

翻译：人工光源常在夜间拍摄的图像中留下强烈的镜头光晕伪影，降低视觉质量与视觉算法性能。现有光晕移除方法主要针对日间场景设计，在夜间场景中效果不佳。夜间光晕移除的挑战性源于人工光源独特的亮度与光谱特征，以及光晕的多样形态与图像退化效应。夜间光晕移除数据集的匮乏制约了该关键领域的研究进展。本文提出首个综合性夜间光晕移除数据集Flare7K++，包含962张真实拍摄光晕图像（Flare-R）与7,000张合成光晕图像（Flare7K）。相比Flare7K，Flare7K++能有效消除光源周围的复杂退化现象——这是仅用合成光晕数据难以解决的问题。此外，现有光晕移除流程依赖人工设定的阈值与模糊核参数提取光源，当光源微小或未过曝时容易失效。针对该问题，我们额外提供Flare7K++中的光源标注数据，并提出全新的端到端处理流程，在移除镜头光晕的同时保留光源特征。该数据集与处理流程为后续夜间光晕移除研究提供了重要基础与基准平台。大量实验证明，Flare7K++丰富了现有光晕数据集的多样性，将夜间光晕移除的前沿研究推向真实应用场景。