  Visibility in hazy nighttime scenes is frequently reduced by multiple
factors, including low light, intense glow, light scattering, and the presence
of multicolored light sources. Existing nighttime dehazing methods often
struggle with handling glow or low-light conditions, resulting in either
excessively dark visuals or unsuppressed glow outputs. In this paper, we
enhance the visibility from a single nighttime haze image by suppressing glow
and enhancing low-light regions. To handle glow effects, our framework learns
from the rendered glow pairs. Specifically, a light source aware network is
proposed to detect light sources of night images, followed by the APSF
(Atmospheric Point Spread Function)-guided glow rendering. Our framework is
then trained on the rendered images, resulting in glow suppression. Moreover,
we utilize gradient-adaptive convolution, to capture edges and textures in hazy
scenes. By leveraging extracted edges and textures, we enhance the contrast of
the scene without losing important structural details. To boost low-light
intensity, our network learns an attention map, then adjusted by gamma
correction. This attention has high values on low-light regions and low values
on haze and glow regions. Extensive evaluation on real nighttime haze images,
demonstrates the effectiveness of our method. Our experiments demonstrate that
our method achieves a PSNR of 30.38dB, outperforming state-of-the-art methods
by 13% on GTA5 nighttime haze dataset. Our data and code is available at
https://github.com/jinyeying/nighttime_dehaze.
