Attention Mechanism-Based Multi-Stage Deblurring for High-Confidence Railway Image Data Enhancement

Image processing is a key component of intelligent perception in rail transit. However, vibrations caused by train movement, particularly during long-focus or distant image capturing, often result in significant image blur, affecting the quality of the data. This paper addresses this challenge by proposing a multi-stage image deblurring method based on attention mechanisms, which is specifically designed to counteract the blurring effects induced by vibrations during train operation. The proposed method uses attention mechanisms to effectively guide the reconstruction of blurred image details and employs a multi-stage approach to minimize various types of motion-induced blur. By applying this deblurring method to images captured by ordinary imaging cameras, which are cost-effective and easy to deploy, the quality of the images can be significantly improved. This enhancement not only supports more accurate intelligent perception in rail transit systems but also provides high-confidence data for system identification and monitoring. The approach offers a promising solution for improving image quality, enabling more reliable and widespread deployment of railway monitoring systems for safe train operations.