An unsupervised volume-to-volume conversion technique based on clinical CT imaging data was presented [17]. Especially for metal workpieces from many dental fillings, a three-dimensional adversarial network with a regularized loss function was designed. A domain transformation technique based on CycleGAN was proposed [18]. The original uncorrected (UC) CT pictures and the DL-MAR images' artifact index were contrasted. By introducing a new artifact unentanglement network, [19] proposed the first unsupervised learning MAR technique. Metal artifacts from CT scans in potential space can be untangled using this network. Additionally, a multi-domain approach combinesdata from various sources to enhance artifact reduction. A double convolutional neural network approach was presented [20]. One of the CNNs functions in the reconstructed image domain and the other in the sinusoidal graph domain. This is especially made for intraoperative cone-beam computed tomography settings and physics. In the image and sinogram domains, an efficient MAR technique based on the fully convolutional network (FCN) was investigated [21]. There are three primary steps in this method. Metal trace segmentation, FCN-based restoration in the sinusoidal graph domain, and FCN-based restoration and metal insertion in the image domain are the first three steps. A framework based on the image domain and the sine graph domain was proposed [22]. This framework trains a neural network (SinoNet) to restore the projection of the metal effect and is tailored to a sinogram completion challenge. A novel dual-domain adaptive scaling nonlocal network (DAN-Net) was proposed [23]. To preserve more tissue and bone details, it first repairs the damaged sinogram using adaptive scaling. The associated reconstructed images are then produced by the analytical reconstruction layer after the sinogram is processed successively using an end-to-end dual-domain network. However, a lot of these networks are computationally demanding and frequently not physically interpretable, which makes them less appropriate for industrial applications with limited resources or in real time. In this paper, a new lightweight CT artifact reduction framework that combines a tailored small Enhanced Deep Super-Resolution (EDSR-Tiny) network with Radon transform theory is proposed. To improve spatial resolution, the entire procedure starts with a bicubic interpolation of low-resolution CT slices. The Radon transform is then used to turn the interpolated slices into sinograms. In the sinogram domain, we employ an artifact removal technique to deal with the corruption caused by metal. The corrected sinogram is then restored with improved fidelity using a self-designed lightweight EDSR-Tiny network that includes a single-layer residual structure and a Radon-consistent loss function. Lastly, the artifact-reduced CT images are reconstructed using the inverse Radon transform. Peak signal-to-noise ratio (PSNR) and signal-to-noise ratio (SNR), two widely used picture quality metrics, are employed to assess the efficacy of this method. Results from experiments on actual industrial CT datasets show that the method proposed maintains low computational complexity while achieving competitive artifact suppression and detail preservation performance. This paper's contributions include the following:  
* A lightweight Radon-domain MAR framework that achieves high-quality CT reconstruction with minimal processing overhead by fusing deep learning and physical modeling. * A specially designed EDSR-Tiny network for sinogram restoration that has a single residual layer and a Radon-consistent loss function.