Print Email Facebook Twitter X-Ray image denoising with directional support value transform Title X-Ray image denoising with directional support value transform Author Zheng, S. Hendriks, E.A. Lei, B. Hao, W. Faculty Electrical Engineering, Mathematics and Computer Science Department Information and Communication Theory Group Date 2009-10-30 Abstract Under the support vector machine framework, the support value analysis-based image fusion has been studied, where the salient features of the original images are represented by their support values. The support value transform (SVT)-based image fusion approach have demonstrated some advantages over the existing methods in multisource image fusion. In this paper, the directional support value transform (DSVT) is applied to the denoising of some standard images embedded in white noise and the X-ray images. This directional transform is not norm-preserving and, therefore, the variance of the noisy support values will depend on the scales. And then we use the hard-thresholding rule for estimating the unknown support values in different scales and the thresholding is scale-dependent. The peak signal noise ratio (PSNR) is used as an “objective” measure of performance, and our own visual capabilities are used to identify artifacts whose effects may not be well-quantified by the PSNR value. The experimental results demonstrate that simple thresholding of the support values in the proposed method is very competitive with techniques based on wavelets, including thresholding of decimated or undecimated wavelet transforms. Subject weighted least squares support vector machinedirectional support value transformimage denoising To reference this document use: http://resolver.tudelft.nl/uuid:a6ed2aac-9a2c-4b11-80a1-88d9724d1a42 Publisher SPIE ISSN 0277-786X Source Proceedings of SPIE, 2009 vol. 7497 Part of collection Institutional Repository Document type conference paper Rights (c)2009 Zheng, S., Hendriks, E.A., Lei, B., Wei, H. Files PDF XrayZheng2009.pdf 7.15 MB Close viewer /islandora/object/uuid:a6ed2aac-9a2c-4b11-80a1-88d9724d1a42/datastream/OBJ/view