## 1 Introduction  
Industrial computed tomography (CT) technology plays an essential role in industrial inspection and quality control, and is particularly irreplaceable in non-destructive testing and the analysis of internal material defects [1]. However, when CT systems scan objects containing metallic materials, strong metal artifacts commonly emerge in the CT images [2]. These artifacts, resulting from uneven X-ray absorption due to materials with high atomic numbers, scatter, and shortcomings within the reconstruction algorithms themselves [3], can severely disrupt image quality, leading to the misinterpretation of image information. This poses significant challenges to subsequent processing and ultimately limits the effectiveness of CT in practical industrial applications. Metal artifact reduction (MAR) has been an active research area within the field of CT image processing. Existing methodologies are broadly categorized into two groups: traditional image processing techniques and deep learning-based methods. Traditional algorithms often concentrate on pre-processing the projection data, including interpolation techniques [4], iterative reconstruction methods [5], model-based artifact correction [6], and frequency domain filtering [7, 8]. For instance, linear interpolation and sinogram interpolation methods attempt to compensate data in regions affected by artifacts. Iterative reconstruction approaches, on the other hand, try to address the data loss caused by metal material by incorporating different mathematical models, such as statistical and geometrical models. However, these traditional methods may be constrained by their inherent model assumptions and may only exhibit effective correction in specific scenarios. In recent years, the rise of deep learning technologies such as convolutional neural networks (CNNs) has led to rapid advancements in deep learning-based MAR methods [9; 10; 11; 12]. Deep learning approaches demonstrate superior capabilities in image quality and processing speed by autonomously learning artifact characteristics and correction mappings. Although they significantly outperform traditional methods in performance metrics, they still rely heavily on a single source of information and do not fully exploit the rich information within the data. In the projection domain, deep learning models necessitate the direct handling of projection data, potentially leading to the neglect of spatial information in the image domain [13]. Deep learning-based corrections focused solely on the projection domain may be ineffective in addressing specific image domain artifacts due to the indirect nature of projection domain information, with changes that might be intricate in the image domain [14]. Such single-domain processing can miss correction errors caused by different factors:  
* Beam Hardening: The nonlinear effects caused by varying attenuation characteristics of different materials are often not apparent in the projection domain. * Scattering: Some scattering effects may be more readily identified and corrected in the image domain. * Statistical Noise: The randomness of noise means that noise reduction within the projection domain might not cater to variations in local image characteristics. * Reconstruction Algorithm Shortcomings [15]: For example, the filters used in filtered back projection (FBP) may not entirely eliminate streaks caused by metal. Conversely, deep learning methods based solely on the image domain might overlook key physical characteristics of the projection data. This limitation arises due to incomplete information, with corrections in the image domain potentially losing part of the raw measurement data, especially when the information has been severely distorted by strong artifacts [16]. Moreover, local optimization in the image domain relies heavily on image features themselves, which could lead to overfitting on local features while ignoring global consistency issues [17].