A clear restoration algorithm based on deep fusion network is proposed to solve the problem of over enhance- ment and insufficient applicability of the existing dust and fog image clear restoration algorithm. The deep fusion net- work mainly includes three parts,namely,the image pre-processing module,the feature fusion module,and the image output module. The image pre-processing module processes the input image based on the contrast enhancement func- tion,the brightness enhancement function,and the gamma correction function to obtain an image sequence that charac- terizes different enhancement modes and degrees. Because the local information and global information of image need to be taken into account,this paper proposes a double pyramid module which can realize a dual-path context informa- tion extraction on the basis of spatial Pyramid pooling and context information aggregation network. The module consists of two series sub-blocks of dilated convolution,one is composed of a series of small to large scale dilated convolu- tion on multiple scales,and the other is composed of a series of large and small scale void convolution on multiple scales. The image output module mainly processes the features acquired by the feature fusion layer,thereby outputting a three-channel image,that is,a clear image. In order to obtain the training data,this paper builds a large-scale train- ing data set based on the dust fog image formation mechanism with the clear coal mine images. In the process of train- ing,this paper uses the least square error loss function and the content loss function based on VGG network to optimize the network. In order to evaluate the effectiveness of the proposed algorithm based on deep fusion network,six other representative clearing algorithms are selected for comparison. The experimental results show that the proposed algo- rithm outperforms the other six algorithms in subjective evaluation and objective evaluation,which indicates that the proposed algorithm can effectively solve the over-enhancement phenomenon and improve the clarity and visualization of coal mine images.