  This paper proposes a novel technique for single image dehazing. Most of the
state-of-the-art methods for single image dehazing relies either on Dark
Channel Prior (DCP) or on Color line. The proposed method combines the two
different approaches. We initially compute the dark channel prior and then
apply a Nearest-Neighbor (NN) based regularization technique to obtain a smooth
transmission map of the hazy image. We consider the effect of airlight on the
image by using the color line model to assess the commitment of airlight in
each patch of the image and interpolate at the local neighborhood where the
estimate is unreliable. The NN based regularization of the DCP can remove the
haze, whereas, the color line based interpolation of airlight effect makes the
proposed system robust against the variation of haze within an image due to
multiple light sources. The proposed method is tested on benchmark datasets and
shows promising results compared to the state-of-the-art, including the deep
learning based methods, which require a huge computational setup. Moreover, the
proposed method outperforms the recent deep learning based methods when applied
on images with sky regions.
