Independent component analysis based filtering for penumbral imaging
Independent component analysis based filtering for penumbral imaging
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基于独立分量分析的半影成像滤波
DOI：
10.1063/1.1787932
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发表时间：
2004
影响因子：
1.6
通讯作者：
S. Nozaki
中科院分区：
文献类型：
--
作者：
Yenwei Chen;X. Han;S. Nozaki
提出了一种基于独立分量分析（伊卡）的泊松噪声滤波方法.在所提出的滤波，图像首先被转换到伊卡域，然后通过软阈值（收缩）去除噪声分量。建议的过滤器，这是用来作为重建的预处理，已成功地应用于半影成像。仿真结果和实验结果表明，与不加去噪滤波器相比，重建图像有了明显的改善。
We propose a filtering based on independent component analysis (ICA) for Poisson noise reduction. In the proposed filtering, the image is first transformed to ICA domain and then the noise components are removed by a soft thresholding (shrinkage). The proposed filter, which is used as a preprocessing of the reconstruction, has been successfully applied to penumbral imaging. Both simulation results and experimental results show that the reconstructed image is dramatically improved in comparison to that without the noise-removing filters.
基于启发式方法的X射线半影图像盲重建
DOI：
--
发表时间：
2003
期刊：
Review of Scientific Instruments 74
影响因子：
--
作者：
Shinya Nozaki;Yen-Wei Chen;Zensho Nakao;Ryosuke Kodama;Hiroyuki Shiraga
通讯作者：
Hiroyuki Shiraga