X-ray image denoising with directional support value transform

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.