X-ray computed tomography (CT) has become a fundamental tool in medical diagnosis, biological research, and industrial inspection due to its high resolution and non-destructive nature. However, conventional high-resolution CT imaging relies on dense projection data acquisition, resulting in prolonged imaging times and increased risk of radiation damage to samples. To balance image quality and radiation safety, sparse-sampling CT reduces the amount of projection data, thereby lowering radiation exposure and shortening imaging duration. Yet, this approach often introduces severe aliasing artifacts in reconstructed images, hindering further structural analysis. To address this issue, this study proposes an enhanced non-local means algorithm that incorporates an adaptive anisotropic field-of-view (FOV) kernel and a bilateral weighting function to effectively suppress aliasing artifacts in sparse-sampled CT images. The algorithm dynamically adjusts the FOV kernel to capture local structural features, significantly improving feature fidelity compared to traditional isotropic FOV kernels. Additionally, by employing a bilateral weighting strategy, the algorithm assigns higher weights to similar image patches, enhancing noise suppression while preserving critical details. Experimental results demonstrate that the proposed method substantially improves image quality on both simulated and experimental datasets, with minimal disruption to original structural details. Quantitative evaluation shows improvements of 15.9% in contrast-to-noise ratio (CNR) and 7.2% in structural similarity (SSIM) index compared to the classical non-local means algorithm, confirming its potential for sparse-sampled CT imaging. The proposed enhanced non-local means algorithm effectively improves reconstructed image quality and provides a viable solution for artifact suppression in sparse-sampling CT.