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PURPOSE In real-time MRI serial images are generally reconstructed from highly undersampled datasets as the iterative solutions of an inverse problem. While practical realizations based on regularized nonlinear inversion (NLINV) have hitherto been surprisingly successful, strong assumptions about the continuity of image features may affect the temporal(More)
Tensor-driven anisotropic diffusion and regularisation have been successfully applied to a wide range of image processing and computer vision tasks such as denoising, inpainting, and optical flow. Empirically it has been shown that anisotropic models with a diffusion tensor perform better than their isotropic counterparts with a scalar-valued diffusivity(More)
0167-8655/$ see front matter 2011 Elsevier B.V. A doi:10.1016/j.patrec.2011.04.004 ⇑ Corresponding author. E-mail addresses: housenli@ymail.com, lihousen yahoo.cn (H. Jiang), rbarrio@unizar.es (R. Barrio), clzch sufang@lsec.cc.ac.cn (F. Su). Recent years have witnessed great success of manifold learning methods in understanding the structure of(More)
Polynomials are widely used in scientific computing and engineering. In this paper, we present an accurate and fast compensated algorithm to evaluate bivariate polynomials with floating-point coefficients. This algorithm is applying error free transformations to the bivariate Horner scheme and sum the final decomposition accurately. We also prove the(More)
This paper proposes a compensated algorithm to evaluate Bézier tensor product surfaces with floating-point coefficients and coordinates. This algorithm is based on the application of error-free transformations to improve the traditional de Casteljau tensor product algorithm. This compensated algorithm extends the compensated de Casteljau algorithm for the(More)
Many modern applications of magnetic resonance imaging (MRI) require high temporal and spatial resolution. Because the data acquisition speed is fundamentally limited by physical and physiological constraints it is important to find approaches to reduce the amount of acquisitions without deteriorating the image quality. Many existing solution strategies are(More)
Designing fast singular value decomposition (SVD) is significantly interesting in applications. The random direct SVD (RSVD) has provided a fast scheme to compute the well-approximate SVD by unilateral randomized sampling. In this paper, we present an efficient random algorithm in a bilateral sampling way. We also prove that the proposed algorithms can be(More)
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