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An ICA-Domain Shrinkage Based Poisson-Noise Reduction Algorithm and Its Application to Penumbral Imaging Xian-Hua HAN Zensho NAKAO Yen-Wei CHEN Ryosuke KODAMA Publication IEICE TRANSACTIONS on Information and Systems Vol.E88-D No.4 pp.750-757 Publication Date: 2005/04/01 Online ISSN: DOI: 10.1093/ietisy/e88-d.4.750 Print ISSN: 0916-8532 Type of Manuscript: PAPER Category: Image Processing and Video Processing Keyword: penumbral imaging, ICA-domain shrinkage, independent component analysis, signal-dependent noise, Full Text: PDF(1.9MB)>>
Summary: 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. | ||||||||||
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