Lightweight super resolution method based on blueprint separable convolution for mine image

In the complex confined space of underground coal mine, some environmental factors such as uneven illumination of artificial light source, heavy concentration of dust and fog in the working face, and complex electromagnetic interference can seriously affect the high-definition imaging of underground surveillance video. Aiming at the problems of low resolution and blurring of mine images affected by complex environment, and the problems that the current image super-resolution reconstruction methods mostly improve the reconstruction effect at the expense of compromising network depth and width, which will increase the complexity of the algorithm greatly, as well as the memory usage of the network model, making it difficult to apply to actual edge mobile devices. In this study, a lightweight super-resolution reconstruction method of mine images based on blueprint separation convolution is proposed. Firstly, the high-efficiency blueprint separation convolution is used to replace the standard convolution in the residual block, and a lightweight residual attention module is designed by introducing coordinate attention mechanism and adding skip connections to improve the residual block, so that the model can achieve a better feature extraction ability while keeping low parameters and computation. Secondly, an enhanced hierarchical feature fusion module is designed to integrate local features first and then global features of different hierarchical features in the network, which can further promote the information flow in the network and enhance the feature utilization rate of the model. Finally, the pixel attention mechanism is added at the end of the network to enhance the attention of the network to the information features, which can improve the feature expression ability of the model and provide more detailed features for the image reconstruction module. Experimental results show that the image quality reconstructed by the lightweight super-resolution reconstruction network based on blueprint separation convolution is not only superior to other comparison algorithms in terms of objective indexes and visual perception, but also achieves a better trade-off between model performance and complexity. When the scaling factor is 4, in comparison to the lightweight algorithm AWSRN-M with the same number of parameters, the average PSNR and SSIM on the coal mine test set are increased by 0.177 2 dB and 0.010 7 respectively, and the floating-point computation is reduced by 66.9%. The results demonstrate that the proposed method can effectively extract the detailed feature information with different levels, achieve the deep fusion of shallow and deep features, and more efficiently reconstruct high-resolution images with clear texture details.