  Haze can degrade the visibility and the image quality drastically, thus
degrading the performance of computer vision tasks such as object detection.
Single image dehazing is a challenging and ill-posed problem, despite being
widely studied. Most existing methods assume that haze has a
uniform/homogeneous distribution and haze can have a single color, i.e. grayish
white color similar to smoke, while in reality haze can be distributed
non-uniformly with different patterns and colors. In this paper, we focus on
haze created by sunlight as it is one of the most prevalent type of haze in the
wild. Sunlight can generate non-uniformly distributed haze with drastic density
changes due to sun rays and also a spectrum of haze color due to sunlight color
changes during the day. This presents a new challenge to image dehazing
methods. For these methods to be practical, this problem needs to be addressed.
To quantify the challenges and assess the performance of these methods, we
present a sunlight haze benchmark dataset, Sun-Haze, containing 107 hazy images
with different types of haze created by sunlight having a variety of intensity
and color. We evaluate a representative set of state-of-the-art image dehazing
methods on this benchmark dataset in terms of standard metrics such as PSNR,
SSIM, CIEDE2000, PI and NIQE. This uncovers the limitation of the current
methods, and questions their underlying assumptions as well as their
practicality.
