  Intense light sources often produce flares in captured images at night, which
deteriorates the visual quality and negatively affects downstream applications.
In order to train an effective flare removal network, a reliable dataset is
essential. The mainstream flare removal datasets are semi-synthetic to reduce
human labour, but these datasets do not cover typical scenarios involving
multiple scattering flares. To tackle this issue, we synthesize a prior-guided
dataset named Flare7K*, which contains multi-flare images where the brightness
of flares adheres to the laws of illumination. Besides, flares tend to occupy
localized regions of the image but existing networks perform flare removal on
the entire image and sometimes modify clean areas incorrectly. Therefore, we
propose a plug-and-play Adaptive Focus Module (AFM) that can adaptively mask
the clean background areas and assist models in focusing on the regions
severely affected by flares. Extensive experiments demonstrate that our data
synthesis method can better simulate real-world scenes and several models
equipped with AFM achieve state-of-the-art performance on the real-world test
dataset.
