摘要
针对稀疏角度投影数据CT图像重建问题,TV-ART算法将图像的梯度稀疏先验知识引入代数重建法(ART)中,对分段平滑的图像具有较好的重建效果。但是,该算法在边界重建时会产生阶梯效应,影响重建质量。因此,本文提出自适应核回归函数结合代数重建法的重建算法(LAKR-ART),不仅在边界重建时不会产生阶梯效应,而且对细节纹理重建具有更好的重建效果。最后对shepp-logan标准CT图像和实际CT头颅图像进行仿真实验,并与ART、TV-ART算法进行比较,实验结果表明本文算法有效。
To the problem of sparse angular projection data of CT image reconstruction, TV-ART algorithm introduces the gradient sparse prior knowledge of image to algebraic reconstruction, and the local smooth image gets a better reconstruction effect. How- ever, the algorithm generates step effect when the borders are reconstructed, affecting the quality of the reconstruction. Therefore, this paper proposes an adaptive kernel regression function combined with Algebraic Reconstruction Technique reconstruction algo- rithm （LAKR-ART）, it does not produce the step effect on the border reconstruction, and has a better effect to detail reconstruc- tion. Finally we use the shepp-logan CT image and the actual CT image to make the simulation experiment, and compare with ART and TV-ART algorithm. The experimental results show the algorithm is of effectiveness.
出处
《计算机与现代化》
2016年第11期38-42,共5页
Computer and Modernization
关键词
图像重建
代数重建法
不完全投影
压缩传感
自适应核回归
image reconstruction
algebraic reconstruction technique
incomplete projection
compressed sensing
adaptive ker-nel regression
