Flare7K: A Phenomenological Nighttime Flare Removal Dataset
- URL: http://arxiv.org/abs/2210.06570v1
- Date: Wed, 12 Oct 2022 20:17:24 GMT
- Title: Flare7K: A Phenomenological Nighttime Flare Removal Dataset
- Authors: Yuekun Dai, Chongyi Li, Shangchen Zhou, Ruicheng Feng, Chen Change Loy
- Abstract summary: We introduce Flare7K, the first nighttime flare removal dataset.
It offers 5,000 scattering and 2,000 reflective flare images, consisting of 25 types of scattering flares and 10 types of reflective flares.
With the paired data, we can train deep models to restore flare-corrupted images taken in the real world effectively.
- Score: 83.38205781536578
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Artificial lights commonly leave strong lens flare artifacts on images
captured at night. Nighttime flare not only affects the visual quality but also
degrades the performance of vision algorithms. Existing flare removal methods
mainly focus on removing daytime flares and fail in nighttime. Nighttime flare
removal is challenging because of the unique luminance and spectrum of
artificial lights and the diverse patterns and image degradation of the flares
captured at night. The scarcity of nighttime flare removal datasets limits the
research on this crucial task. In this paper, we introduce, Flare7K, the first
nighttime flare removal dataset, which is generated based on the observation
and statistics of real-world nighttime lens flares. It offers 5,000 scattering
and 2,000 reflective flare images, consisting of 25 types of scattering flares
and 10 types of reflective flares. The 7,000 flare patterns can be randomly
added to flare-free images, forming the flare-corrupted and flare-free image
pairs. With the paired data, we can train deep models to restore
flare-corrupted images taken in the real world effectively. Apart from abundant
flare patterns, we also provide rich annotations, including the labeling of
light source, glare with shimmer, reflective flare, and streak, which are
commonly absent from existing datasets. Hence, our dataset can facilitate new
work in nighttime flare removal and more fine-grained analysis of flare
patterns. Extensive experiments show that our dataset adds diversity to
existing flare datasets and pushes the frontier of nighttime flare removal.
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