FlareReal: A Real-Captured Paired Dataset for Nighttime Lens Flare Removal

Nighttime lens flare removal is difficult to study with supervised learning because real flare-corrupted and flare-free image pairs are hard to acquire with accurate alignment. Existing benchmarks therefore rely mainly on synthetic compositing or rendering, but these pipelines cannot fully reproduce the coupled effects of lens contamination, internal reflection, sensor saturation, automatic exposure, and diverse urban light sources. We introduce FlareReal, a real-captured benchmark for nighttime flare removal, containing 4,037 pixel-aligned flare-corrupted/flare-free pairs and 500 real pure flare images. FlareReal is collected through a controlled contamination-cleaning protocol: each scene is first photographed with physically induced lens contamination and then immediately re-captured after optical cleaning, followed by robust global alignment and manual curation. The dataset covers 241 scenes, multiple smartphone lens systems, point/linear/area light sources, single- and multi-source layouts, and challenging exposure conditions. Experiments across representative restoration architectures show that models trained with FlareReal consistently outperform models trained on Flare7K, Flare7K++, and FlareX on Flare7K, FlareX, and FlareReal test sets, and further improve generalization under cross-device and off-screen settings. These results suggest that real paired supervision captures transferable flare formation cues that are difficult to obtain from synthetic data alone.