  Haze and fog reduce the visibility of outdoor scenes as a veil like
semi-transparent layer appears over the objects. As a result, images captured
under such conditions lack contrast. Image dehazing methods try to alleviate
this problem by recovering a clear version of the image. In this paper, we
propose a Fully Convolutional Neural Network based model to recover the clear
scene radiance by estimating the environmental illumination and the scene
transmittance jointly from a hazy image. The method uses a relaxed haze imaging
model to allow for the situations with non-uniform illumination. We have
trained the network by minimizing a custom-defined loss that measures the error
of reconstructing the hazy image in three different ways. Additionally, we use
a multilevel approach to determine the scene transmittance and the
environmental illumination in order to reduce the dependence of the estimate on
image scale. Evaluations show that our model performs well compared to the
existing state-of-the-art methods. It also verifies the potential of our model
in diverse situations and various lighting conditions.
