Objective: To solve the problem of motion blur in the monitoring image of the pantograph carbon slide caused by the fast running speed of high-speed railway, an image deblurring method based on improved multi-stage progressive network is proposed. Methods: First, a hybrid dilated convolution is introduced as a feature extraction network, which can increase the local receptive field without changing the calculation and resolution of the feature map, and then obtain high-quality image texture and detail information. Secondly, the pixel attention mechanism was introduced to adaptively select the weight value of each pixel to enhance the deblurring quality of the model. Thirdly, a hybrid loss function was introduced to improve the robustness of the model to different types of fuzziness. Finally, a synthetic data set of 1 600 pairs of pantograph carbon slide monitoring images was made for the model to train and test. The experimental results show that the peak signal-to-noise ratio (PSNR) reaches 38.82 dB and the structural similarity (SSIM) reaches 0.972 3. Compared with the other seven classical methods, the proposed network can better restore the edge contour and texture detail information of the image. The robustness of the model is effectively improved.