Image processing method for defect images of ferromagnetic metal casings based on pulsed eddy current testing

In oil and gas extraction, ferromagnetic metal casings serve as critical infrastructure to ensure the safety of hydrocarbon transport. However, under high-temperature and high-pressure conditions, casings buried deep underground are prone to deformation, twisting, and even rupture due to erosion and corrosion, potentially leading to significant economic losses and safety hazards. Therefore, regular inspection and maintenance of in-service well casings are essential. Pulsed eddy current testing (PECT) has been widely used for casing defect detection owing to its efficiency, non-contact nature, and rich information content. However, the presence of substantial noise during detection degrades the quality of defect detection images. To address this issue, we investigated image processing techniques for casing defect detection images and proposed an image processing algorithm (BIC) based on bidimensional empirical mode decomposition (BEMD), improved wavelet threshold denoising (IWTD), and contrast limited adaptive histogram equalization (CLAHE). The proposed method first applied BEMD-IWTD for noise suppression in defect detection images, followed by CLAHE for image enhancement. To validate the effectiveness of the method, defect detection experiments were conducted on casings with ring-shaped and local defects, and the acquired images were processed. After being processed with the BIC algorithm, ring-shaped defects of different depths could be effectively distinguished, especially the 1 mm and 2 mm deep defects that were previously affected by noise. In the local defect images, small-sized defects difficult to be identified due to noise interference were successfully recognized, and the defect contrast C_d was significantly improved. The results demonstrate that the proposed BIC algorithm effectively suppresses the noise in defect detection images, enhances the contrast between defects and the background, and improves defect recognition and detection accuracy, providing reliable image processing support for subsequent defect analysis.