A Beam Hardening Artifact Correction Method for CT Images Based on VGG Feature Extraction Networks

In X-ray industrial computed tomography (ICT) imaging, beam hardening artifacts significantly degrade the quality of reconstructed images, leading to cupping effects, ring artifacts, and reduced contrast resolution. These issues are particularly severe in high-density and irregularly shaped aerospace components, where accurate defect detection is critical. To mitigate beam hardening artifacts, this paper proposes a correction method based on the VGG16 feature extraction network. Continuous convolutional layers automatically extract relevant features of beam hardening artifacts, establish a nonlinear mapping between artifact-affected and artifact-free images, and progressively enhance the model's ability to understand and represent complex image features through stacked layers. Then, a dataset of ICT images with beam hardening artifacts is constructed, and VGG16 is employed to extract deep features from both artifact-affected and reference images. By incorporating perceptual loss into a convolutional neural network and optimizing through iterative training, the proposed method effectively suppresses cupping artifacts and reduces edge blurring. Experimental results demonstrated that the method significantly enhanced image contrast, reduced image noise, and restored structural details, thereby improving the reliability of ICT imaging for aerospace applications.