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Abstract Pipelines represent the main mode of transportation for oil and gas, and failure caused by weld defects is the primary cause of accidents, presenting significant risks to personal and environmental safety. Therefore, regularly inspecting pipeline welds is essential for reducing accidents, ensuring personal safety, protecting the environment, and achieving sustainable development. Although manual photographic X-ray inspection is widely utilized for the detection of weld defects in various industries, this process is challenging since X-ray images are noisy and unclear, with uneven grey values. This study proposed a noise reduction framework by introducing a Wiener filter into the wavelet domain to reduce noise in X-ray images while minimizing information loss. Furthermore, a comprehensive evaluation factor that combined contrast and the noise reduction level was proposed to reduce the dependence of image processing performance on wavelet thresholds. Additionally, this study improved the Laplace method by adaptively adjusting the normal and tangential diffusion coefficients to enhance the weld X-ray image contrast without increasing the noise. Through qualitative comparison and quantitative analysis, it has been determined that the suggested methods exhibit better properties than alternative industrial pipeline weld X-ray image processing algorithms. This superiority is observed in objective values as well as subjective visualizations.
Xie et al. (Wed,) studied this question.
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