The reliability of rotary drilling rigs often hinges on the welding quality of their critical component, the mast. X-ray testing is employed to inspect key welds on the mast, but due to objective constraints, the acquired Xray images frequently contain significant noise. Traditional Gaussian filtering methods, plagued by issues such as blurred edges and insufficient versatility, often fall short. To address these deficiencies, this study introduced three innovative Gaussian filtering techniques: adaptive Gaussian filtering, Gaussian pyramid-based and image fusion Gaussian filtering, and guided Gaussian filtering. Analysis using a series of quality assessment metrics demonstrated that Gaussian pyramid-based and image fusion Gaussian filtering, as well as guided Gaussian filtering, offer superior performance. These results provide new avenues for enhancing traditional Gaussian filtering effects and improving the subsequent accuracy of weld seam identification in rotary drilling rigs.