电子测量与仪器学报

姚圣坤,王书伟,邹杨,张家乐.增强型非局部均值算法去除稀疏采样X射线CT图像中的混叠伪影[J].电子测量与仪器学报,2026,40(2):184-196

增强型非局部均值算法去除稀疏采样X射线CT图像中的混叠伪影

Enhanced non-local means algorithm for suppressing aliasingartifacts in sparse-sampled X-ray CT images

DOI：

英文关键词:X-ray CT sparse sampling artifacts suppression non-local means non-isotropic foveation kernel

基金项目:国家自然科学基金（12004227）项目资助

作者	单位
姚圣坤	1.山东师范大学物理与光电学院济南250358；2.山东省光场调控物理及应用重点实验室济南250358
王书伟	1.山东师范大学物理与光电学院济南250358；2.山东省光场调控物理及应用重点实验室济南250358
邹杨	1.山东师范大学物理与光电学院济南250358；2.山东省光场调控物理及应用重点实验室济南250358
张家乐	1.山东师范大学物理与光电学院济南250358；2.山东省光场调控物理及应用重点实验室济南250358

Author	Institution
Yao Shengkun	1.School of Physics and Optoelectronics, Shandong Normal University, Jinan 250358, China; 2.Shandong Provincial Key Laboratory of Light Field Manipulation Physics and Applications, Jinan 250358, China
Wang Shuwei	1.School of Physics and Optoelectronics, Shandong Normal University, Jinan 250358, China; 2.Shandong Provincial Key Laboratory of Light Field Manipulation Physics and Applications, Jinan 250358, China
Zou Yang	1.School of Physics and Optoelectronics, Shandong Normal University, Jinan 250358, China; 2.Shandong Provincial Key Laboratory of Light Field Manipulation Physics and Applications, Jinan 250358, China
Zhang Jiale	1.School of Physics and Optoelectronics, Shandong Normal University, Jinan 250358, China; 2.Shandong Provincial Key Laboratory of Light Field Manipulation Physics and Applications, Jinan 250358, China

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中文摘要:

X射线计算机断层扫描成像(computed tomography, CT)凭借高分辨率与非破坏性特性，已成为医学诊断、生物研究及工业检测领域的核心工具。然而，高分辨率CT成像依赖密集投影数据采集，导致成像时间延长，容易引起被测样品的辐射损伤。为了平衡图像质量与辐射安全，稀疏采样CT可以减少投影数据量，降低了辐射暴露与成像时间，但重建图像中会出现严重的混叠伪影，制约了进一步结构分析。为此，提出一种增强型非局部均值算法，结合自适应各向异性视场(field-of-view, FOV)核与双边权重函数，有效抑制稀疏采样CT图像中的混叠伪影。该算法通过动态调整FOV核捕捉局部结构特征，较传统各向同性FOV核显著提升特征保真度。同时，该算法基于双边权重策略，实现了相似图像块获得更高权重，在保留关键细节的同时增强噪声抑制效果。实验结果表明，算法在模拟与实验数据集上均显著提升图像质量，且对原始结构细节的干扰极小。算法在对比度噪声比(contrast-to-noise ratio, CNR)与结构相似性(structural similarity, SSIM)指标上相比经典非局部均值算法提升15.9%与7.2%，验证了其在稀疏采样CT成像中的应用潜力。所提出的增强型非局部均值算法有效提升图像质量，为稀疏采样CT的伪影抑制提供了有效解决方案。

英文摘要:

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.

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