关键词: 雾天交通图像, 图像去雾, 卷积神经网络, 深度图

Abstract: For the current dehazing algorithm easily results in low brightness of near road area and distant sky area with strong dehazing, and high brightness of middle and distant area with weak dehazing, based on a depth learning dehazing algorithm, a dehazing algorithm combined with image scene depth and road image characteristics of fog and sky roads was proposed. Firstly, based on the principle of dehazing algorithm of deep learning, a convolution neural network was constructed to calculate the scene transmittance. And then the image depth map was estimated based on the transmittance and atmospheric scattering model. Two parameters were constructed, the upper threshold and the lower threshold, to divide the depth map into middle, far, and near areas. Based on the enhancement function constructed by the different parts of the depth map, the enhancement amplitude of image processing was determined. Finally, based on the traditional atmospheric scattering model, the intensified illumination intensity was used to adjust the recovery intensity of different areas to obtain the optimized image. The experimental results show that the proposed algorithm is as good as other representative dehazing algorithms and enhance the middle and distant areas of the road image better. It effectively solves the color distortion and low contrast ratio of the near road surface and distant sky in the foggy road image, improves the visual effect of the reconstructed image, and has better image sharpening effect than dark channel prior algorithm, vision enhancement algorithm for homogeneous and heterogeneous fog, and typical dehazing algorithm based on deep learning.

Key words: haze traffic image, image dehazing, Convolutional Neural Network (CNN), depth map