  Image dehazing is a crucial image pre-processing task aimed at removing the
incoherent noise generated by haze to improve the visual appeal of the image.
The existing models use sophisticated networks and custom loss functions which
are computationally inefficient and requires heavy hardware to run. Time is of
the essence in image pre-processing since real time outputs can be obtained
instantly. To overcome these problems, our proposed generic model uses a very
light convolutional encoder-decoder network which does not depend on any
atmospheric models. The network complexity-image quality trade off is handled
well in this neural network and the performance of this network is not limited
by low-spec systems. This network achieves optimum dehazing performance at a
much faster rate, on several standard datasets, comparable to the
state-of-the-art methods in terms of image quality.
