A single-image defogging algorithm based on deep learning is proposed. The convolutional neural network achieves defogging by learning the mapping relationship among the YUV(Y is luminance, UV is chrominance) channels of the foggy and clear images. The network structure comprises two identical feature modules, which mainly include multi-scale convolution, convolution and skip-connection frameworks. The experimental results show that the proposed algorithm can be used to restore images with high resolution and high contrast, regardless of the datasets with synthetic or natural fog images. Furthermore, a comparative evaluation of this algorithm with the existing algorithms confirms its superior performance both subjectively and objectively.