An ICA-Domain Shrinkage Based Poisson-Noise Reduction Algorithm and Its Application to Penumbral Imaging
An ICA-Domain Shrinkage Based Poisson-Noise Reduction Algorithm and Its Application to Penumbral Imaging
复制标题
基于ICA域收缩的泊松降噪算法及其在半影成像中的应用
DOI：
10.1093/ietisy/e88-d.4.750
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
R. Kodama
中科院分区：
文献类型：
--
作者：
X. Han;Z. Nakao;Yenwei Chen;R. Kodama
半影成像是一种利用空间信息可以从未知源通过简单的大圆孔投射的阴影或半影中恢复的技术。由于该技术是基于线性反卷积，它是敏感的噪声。本文提出了一种两步解码半影图像的方法：首先，应用基于ICA域（独立分量分析域）收缩的降噪算法来平滑给定的噪声;其次，进行常规的线性反卷积。仿真结果表明，与不加去噪滤波器相比，该方法重建的图像质量有了明显改善，并成功地应用于真实的实验X射线成像。
Penumbral imaging is a technique which exploits the fact that spatial information can be recovered from the shadow or penumbra that an unknown source casts through a simple large circular aperture. Since the technique is based on linear deconvolution, it is sensitive to noise. In this paper, a two-step method is proposed for decoding penumbral images: first, a noise-reduction algorithm based on ICA-domain (independent component analysis-domain) shrinkage is applied to smooth the given noise; second, the conventional linear deconvolution follows. The simulation results show that the reconstructed image is dramatically improved in comparison to that without the noise-removing filters, and the proposed method is successfully applied to real experimental X-ray imaging.