Abstract: The continuous expansion of heavy-haul railway video surveillance systems has led to a sharp increase in railway video data, with higher requirements for data storage and transmission capabilities. To this end, this paper proposed a compressed video quality enhancement method for heavy-haul railway surveillance based on optical flow guided Transformer model: Extracting inter frame motion information through optical flow completion network to guide the Transformer model to focus on important features in the video sequence; Combining multi head self-attention mechanism and spatiotemporal feature fusion strategy, effectively extracting spatiotemporal features of video frames; By incorporating optical flow guided feature enhancement modules into the Transformer model structure, further improving the accuracy and efficiency of video quality enhancement. The experimental results based on the actual collected heavy-duty railway surveillance video dataset show that this method is significantly superior to existing video quality enhancement methods and has practical value.