From Generation to Suppression: Towards Effective Irregular Glow Removal
  for Nighttime Visibility Enhancement
Wanyu Wu Wei Wang Zheng Wang Kui Jiang Xin Xu
Most existing Low-Light Image Enhancement (LLIE) methods are primarily
designed to improve brightness in dark regions, which suffer from severe
degradation in nighttime images. However, these methods have limited
exploration in another major visibility damage, the glow effects in real night
scenes. Glow effects are inevitable in the presence of artificial light sources
and cause further diffused blurring when directly enhanced. To settle this
issue, we innovatively consider the glow suppression task as learning physical
glow generation via multiple scattering estimation according to the Atmospheric
Point Spread Function (APSF). In response to the challenges posed by uneven
glow intensity and varying source shapes, an APSF-based Nighttime Imaging Model
with Near-field Light Sources (NIM-NLS) is specifically derived to design a
scalable Light-aware Blind Deconvolution Network (LBDN). The glow-suppressed
result is then brightened via a Retinex-based Enhancement Module (REM).
Remarkably, the proposed glow suppression method is based on zero-shot learning
and does not rely on any paired or unpaired training data. Empirical
evaluations demonstrate the effectiveness of the proposed method in both glow
suppression and low-light enhancement tasks.