Stereo Vision aided Image Dehazing using Deep Neural Network

Deterioration of image due to haze is one of the factors that degrade the performance of computer vision algorithm. The haze component absorbs and reflects the reflected light from the object, distorting the original irradiance. The more the distance from the camera is, the more deteriorated it tends to be. Therefore, studies have been conducted to remove haze by estimating the distribution of haze along the distance. In this paper, we use convolution neural network to simultaneously perform depth estimation and haze removal based on stereo image, and depth information to help improve performance of haze removal. We propose a multitasking network in which the encoder learns depth information and dehazing features simultaneously by performing depth estimation and dehazing using two decoders. The learning of the network is based on a stereo image, and a large amount of left and right hazy images are required. However, existing hazy image data sets are inferior in reality because they are added to fog components in indoor images. Therefore, a data set composed of a haze component corresponding to the distance information was constructed and used in the KITTI road data set composed of a large amount of stereo outdoor driving images. Experimental results show that the proposed network has robust dehazing performance compared to existing methods for various levels of hazy images and improves the visibility by strengthening the contrast of boundaries in faint areas due to haze.