  Haze limits the visibility of outdoor images, due to the existence of fog,
smoke and dust in the atmosphere. Image dehazing methods try to recover
haze-free image by removing the effect of haze from a given input image. In
this paper, we present an end to end system, which takes a hazy image as its
input and returns a dehazed image. The proposed method learns the mapping
between a hazy image and its corresponding transmittance map and the
environmental illumination, by using a multi-scale Convolutional Neural
Network. Although most of the time haze appears grayish in color, its color may
vary depending on the color of the environmental illumination. Very few of the
existing image dehazing methods have laid stress on its accurate estimation.
But the color of the dehazed image and the estimated transmittance depends on
the environmental illumination. Our proposed method exploits the relationship
between the transmittance values and the environmental illumination as per the
haze imaging model and estimates both of them. Qualitative and quantitative
evaluations show, the estimates are accurate enough.
