  Degradation of image quality due to the presence of haze is a very common
phenomenon. Existing DehazeNet [3], MSCNN [11] tackled the drawbacks of hand
crafted haze relevant features. However, these methods have the problem of
color distortion in gloomy (poor illumination) environment. In this paper, a
cardinal (red, green and blue) color fusion network for single image haze
removal is proposed. In first stage, network fusses color information present
in hazy images and generates multi-channel depth maps. The second stage
estimates the scene transmission map from generated dark channels using multi
channel multi scale convolutional neural network (McMs-CNN) to recover the
original scene. To train the proposed network, we have used two standard
datasets namely: ImageNet [5] and D-HAZY [1]. Performance evaluation of the
proposed approach has been carried out using structural similarity index
(SSIM), mean square error (MSE) and peak signal to noise ratio (PSNR).
Performance analysis shows that the proposed approach outperforms the existing
state-of-the-art methods for single image dehazing.
