  In this paper a hybrid image defogging approach based on region segmentation
is proposed to address the dark channel priori algorithm's shortcomings in
de-fogging the sky regions. The preliminary stage of the proposed approach
focuses on the segmentation of sky and non-sky regions in a foggy image taking
the advantageous of Meanshift and edge detection with embedded confidence. In
the second stage, an improved dark channel priori algorithm is employed to
defog the non-sky region. Ultimately, the sky area is processed by DehazeNet
algorithm, which relies on deep learning Convolutional Neural Networks. The
simulation results show that the proposed hybrid approach in this research
addresses the problem of color distortion associated with sky regions in foggy
images. The approach greatly improves the image quality indices including
entropy information, visibility ratio of the edges, average gradient, and the
saturation percentage with a very fast computation time, which is a good
indication of the excellent performance of this model.
