Xie Xu

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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)
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 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)
We propose an alternative volumetric data modeling and reduction approach via compressive sensing theory. We provide evidence that with a small set of randomly chosen Fourier samples of a dataset, it is possible to recover the dataset accurately. Our experiments demonstrate that the number of samples necessary for an accurate reconstruction is linearly(More)
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