Stereo Vision aided Image Dehazing using Deep Neural Network
Jeong-Yun Na, Kuk‐Jin Yoon · 2018
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.