As the core equipment of transmission and distribution hubs, the operational status of gas-insulated switchgear (GIS) is closely linked to the safety of the power system. Recently, X-ray digital imaging technology has been extensively used in GIS equipment fault detection. However, the X-ray image of GIS is blurred, which is not conducive to the detection of tiny defects. Thus, a super-resolution method for GIS X-ray images based on multi-scale context transformers is proposed in this study, namely MCTSR. Firstly, a second-order image degradation model is introduced to generate GIS X-ray low-resolution images that more closely resemble the real world. Secondly, a contextual transformer gate module is constructed to improve attention to tiny defects in GIS X-ray images. Thirdly, a U-Net discriminator network based on multi-scale contextual transformers is intended to enrich the information of the generated images. Finally, the proposed discriminator is combined with the existing generator to compose a super-resolution method applicable to GIS X-ray images. The experimental results demonstrate that the method outperforms other methods in peak signal-to-noise ratio and structural similarity on the constructed GIS X-ray image dataset. In addition, the output image of the proposed method facilitates the subsequent defect detection.

中文翻译：

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MCTSR：基于多尺度上下文变压器的气体绝缘开关设备X射线图像缺陷超分辨率方法

气体绝缘开关设备（GIS）作为输配电枢纽的核心设备，其运行状态与电力系统的安全密切相关。近年来，X射线数字成像技术已广泛应用于GIS设备故障检测。然而GIS的X射线图像模糊，不利于微小缺陷的检测。因此，本研究提出了一种基于多尺度上下文变换器的GIS X射线图像超分辨率方法，即MCTSR。首先，引入二阶图像退化模型来生成更接近现实世界的GIS X射线低分辨率图像。其次，构建了上下文变压器门模块，以提高对 GIS X 射线图像中微小缺陷的关注。第三，基于多尺度上下文变换器的 U-Net 判别器网络旨在丰富生成图像的信息。最后，将所提出的鉴别器与现有的生成器相结合，组成适用于 GIS X 射线图像的超分辨率方法。实验结果表明，该方法在构建的GIS X射线图像数据集上的峰值信噪比和结构相似性方面优于其他方法。此外，该方法的输出图像有利于后续的缺陷检测。实验结果表明，该方法在构建的GIS X射线图像数据集上的峰值信噪比和结构相似性方面优于其他方法。此外，该方法的输出图像有利于后续的缺陷检测。实验结果表明，该方法在构建的GIS X射线图像数据集上的峰值信噪比和结构相似性方面优于其他方法。此外，该方法的输出图像有利于后续的缺陷检测。