  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.
