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：人造光源在夜间拍摄的图像中常会留下强烈的镜头光晕伪影，降低视觉质量与视觉算法性能。现有光晕去除方法主要针对日间光晕，难以处理夜间场景。由于人造光源独特的亮度与光谱特性，以及光晕呈现的多样模式和图像退化效应，夜间光晕去除极具挑战性。夜间光晕去除数据集的匮乏制约了该关键任务的研究进展。本文提出首个综合性夜间光晕去除数据集Flare7K++，包含962张真实拍摄的光晕图像（Flare-R）和7000张合成光晕图像（Flare7K）。相较于Flare7K，Flare7K++在消除光源附近复杂退化方面尤为有效——这类退化仅靠合成光晕难以处理。此外，现有光晕去除流程依赖手动阈值与模糊核设置来提取光源，当光源微小或未过度曝光时可能失效。为解决此问题，我们在Flare7K++中额外提供光源标注，并提出新型端到端流程，在去除镜头光晕的同时保留光源。该数据集与流程为夜间光晕去除领域的未来研究提供了宝贵基础与基准。大量实验表明，Flare7K++补充了现有光晕数据集的多样性，将夜间光晕去除研究推向真实场景前沿。