A mine image reconstruction method based on residual neural network

In order to address the problems of the disturbance from environmental noise on visual operations in under- ground mines,and to satisfy the demands of intelligent mining that call for imaging clarity of the operating objects,it is of significance to investigate into the theories and the methods of the reconstruction of mine video images based on computer vision. This is of vital importance of the advancement in intelligent mine monitoring and safe mining technol- ogies. Conventional video monitoring system adopts classical Nyquist sampling theorem to address the points of video acquisition,compression,and encoding and decoding. However,the conventional compression methods would waste val- uable acquisition resources and increase the system overheads when dealing with data as large as videos. It is more difficult for conventional methods to address the problems of fidelity loss,edge blurring,and transmission latency in re- gards of the reconstruction of the image,which downgrades the performance of the mine surveillance system and the quality of video transmission. In response to these challenges,an image compression and reconstruction method based on residual network is proposed. The proposed method establishes a novel network featuring skipping connections. Down-sampling matrix is applied to compress the original mine image. Then multiple up-sampling layers are inserted to scale up the feature maps to the size of the original image. The feature maps are learnt by residual network blocks. Fi- nally the feature maps are aggregated to the reconstructed image by the optimized network. In addition,a new loss function called discrete wavelet structural similarity ( DW-SSIM) loss is proposed. The DW-SSIM loss and mean square error loss are added up together as the total loss when training the network. The experiments are carried out to validate the effectiveness of the proposed method. The proposed method is compared to the algorithms of the com- pressed sensing based D-AMP,TVAL3,and deep learning based ReconNet. The experiments show that the proposed method surpasses other algorithms at low compression ratios in regards of PSNR ( Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity). As for reconstruction at the present of noise,there are less fluctuations in PSNR and SSIM for the proposed method as the noise level varies. It is concluded that the proposed method features a strong noise robustness and is able to significantly improve the fidelity and clarity of the reconstructed images. Compared to other algorithms,the proposed method consumes the least time,which can improve the real time performance of the mine video surveillance systems.