测试技术学报2025，Vol.39Issue(5)：558-564,572,8.DOI:10.62756/csjs.1671-7449.2025068
基于加权引导滤波与梯度域卷积稀疏的CT重建
CT Reconstruction Based on Weighted Guided Filtering and Gradient Domain Convolution Sparseness
摘要
Abstract
For incomplete medical CT scan data,traditional algorithms cannot ensure that the recon-structed images meet the diagnostic requirements.In order to solve this situation,a CT reconstruction algorithm based on the combination of weighted guided filtering and gradient domain convolutional sparse coding was proposed.Firstly,the penalty least squares method is used to iteratively reconstruct the initial CT image.Secondly,the weighted guided filtering is used to obtain the low-frequency components of the image,and the convolutional sparse coding with gradient constraints is used to process the high-frequency components of the image.Finally,the two components are combined to obtain a new image as input,and the least-squares approximation is continued,and the iterative reconstruction is repeated until a clearer image is obtained.Experimental results show that compared with other convolutional sparse algorithms and group sparse algorithms,the proposed algorithm can effectively suppress noise and artifacts,recover more image structure and edge detail information,and obtain better reconstructed images.关键词
计算机断层成像/卷积稀疏编码/加权引导滤波/稀疏角度/图像重建Key words
computed tomography/convolutional sparse coding/weighted guided filtering/sparse angle/image reconstruction分类
信息技术与安全科学引用本文复制引用
马燕,白艳萍,续婷,程蓉..基于加权引导滤波与梯度域卷积稀疏的CT重建[J].测试技术学报,2025,39(5):558-564,572,8.基金项目
山西省基础研究计划资助项目(202103021224195,202103021224212,202103021223189,20210302123019) （202103021224195,202103021224212,202103021223189,20210302123019）
山西省回国留学人员科研项目(2021-108) （2021-108）
