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Degradation of image quality due to the presence of haze is a very commonphenomenon. Existing DehazeNet [3], MSCNN [11] tackled the drawbacks of handcrafted haze relevant features. However, these methods have the problem ofcolor distortion in gloomy (poor illumination) environment. In this paper, acardinal (red, green and blue) color fusion network for single image hazeremoval is proposed. In first stage, network fusses color information presentin hazy images and generates multi-channel depth maps. The second stageestimates the scene transmission map from generated dark channels using multichannel multi scale convolutional neural network (McMs-CNN) to recover theoriginal scene. To train the proposed network, we have used two standarddatasets namely: ImageNet [5] and D-HAZY [1]. Performance evaluation of theproposed 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 existingstate-of-the-art methods for single image dehazing.

C2MSNet: A Novel approach for single image haze removal