Refining the Transmission Map and Air Light in the Atmospheric Light
Scattering Model for An Efficient Single Image Dehazing Method
Many dehazing methods have been presented by many researchers either on
single hazy images or multiple images like videos. With the increasing
volume of hazy image datasets, recent literature about dehazing is
mostly based on deep learning methods. Although, deep learning models
perform better than traditional methods on the validation data of the
specific dataset which they are trained with, the generalization
performance of deep learning models is generally poor. Another important
bottleneck for deep models is the training cost in terms of time and
hardware requirements. In this study, traditional DCP method is improved
by taking into account the amount of haze and refining the transmission
and air light used in atmospheric light scattering model of haze.
Results of this study shows that proposed method is superior and/or
competitive to traditional DCP method and state of the art deep learning
models in terms of the visual quality of the dehazed image.