Journal of Frontiers of Computer Science and Technology ›› 2021, Vol. 15 ›› Issue (1): 185-194.DOI: 10.3778/j.issn.1673-9418.2001023

• Graphics and Image • Previous Articles    

Single Image Dehazing Method Based on Densely Connected Dilated Convolutional Neural Network

LIU Guangzhou, LI Jinbao, REN Dongdong, SHU Minglei   

  1. 1. Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
    2. College of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
  • Online:2021-01-01 Published:2021-01-07



  1. 1. 齐鲁工业大学(山东省科学院) 山东省人工智能研究院,济南 250014
    2. 黑龙江大学 计算机科学技术学院,哈尔滨 150080


In view of the poor accuracy of model parameter estimation and color distortion in most image dehazing algorithms, an end-to-end dense connected dilated convolutional neural network is proposed. First of all, this paper uses multi-layer dense connection structure to increase the feature utilization of the network and avoid the gradient disappearance when the network deepens. Secondly, by using the dilation convolution with different dilation rates in the dense blocks, the network can fully aggregate the context feature information without losing the spatial resolution, and avoid the generation of mesh artifacts. Finally, in order to improve the ability of the algorithm, this paper divides the network into several stages, and introduces the side output module in each stage, so as to obtain more accurate feature information. The experimental results show that the proposed dehazing algorithm has a good dehazing effect on both the synthetic data set and the real data set. The recovered color is closer to the ground truth, and the quantitative evaluation indexes peak signal to noise ratio (PSNR) and structural similarity index (SSIM) are better than other comparison methods.

Key words: image dehazing, convolutional neural network (CNN), dense connection, dilation convolution



关键词: 图像去雾, 卷积神经网络(CNN), 密集连接, 扩张卷积