  Reflective flare is a phenomenon that occurs when light reflects inside
lenses, causing bright spots or a "ghosting effect" in photos, which can impact
their quality. Eliminating reflective flare is highly desirable but
challenging. Many existing methods rely on manually designed features to detect
these bright spots, but they often fail to identify reflective flares created
by various types of light and may even mistakenly remove the light sources in
scenarios with multiple light sources. To address these challenges, we propose
an optical center symmetry prior, which suggests that the reflective flare and
light source are always symmetrical around the lens's optical center. This
prior helps to locate the reflective flare's proposal region more accurately
and can be applied to most smartphone cameras. Building on this prior, we
create the first reflective flare removal dataset called BracketFlare, which
contains diverse and realistic reflective flare patterns. We use continuous
bracketing to capture the reflective flare pattern in the underexposed image
and combine it with a normally exposed image to synthesize a pair of
flare-corrupted and flare-free images. With the dataset, neural networks can be
trained to remove the reflective flares effectively. Extensive experiments
demonstrate the effectiveness of our method on both synthetic and real-world
datasets.
