Xie Xu

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In this paper, we investigate compressed sensing principles to devise an in-situ data reduction framework for vi-sualization of volumetric datasets. We exploit the universality of the compressed sensing framework and show that the proposed method offers a refinable data reduction approach for volumetric datasets. The accurate reconstruction is obtained from(More)
We present a variational framework for the reconstruction of irregularly-sampled volumetric data in, nontensor-product, spline spaces. Motivated by the sampling-theoretic advantages of body centered cubic (BCC) lattice, this paper examines the BCC lattice and its associated box spline spaces in a variational setting. We introduce a regularization scheme for(More)
We examine different sampling lattices and their respective bandlimited spaces for reconstruction of irregularly sampled multidimensional images. Considering an irregularly sampled dataset, we demonstrate that the non-tensor-product bandlimited approximations corresponding to the body-centered cubic and face-centered cubic lattices provide a more accurate(More)
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