Single-angle Radon samples based reconstruction of functions in refinable shift-invariant space

The traditional approaches to computerized tomography (CT) depend on the samples of Radon transform at multiple angles. In optics, the real time imaging requires the reconstruction of an object by the samples of Radon transform at a single angle (SA). Driven by this and motivated by the connection between Bin Han's construction of wavelet frames (e.g [13]) and Radon transform, in refinable shift-invariant spaces (SISs) we investigate the SA-Radon sample based reconstruction problem. We have two main theorems. The fist main theorem states that, any compactly supported function in a SIS generated by a general refinable function can be determined by its Radon samples at an appropriate angle. Motivated by the extensive application of positive definite (PD) functions to interpolation of scattered data, we also investigate the SA reconstruction problem in a class of (refinable) box-spline generated SISs. Thanks to the PD property of the Radon transform of such spline, our second main theorem states that, the reconstruction of compactly supported functions in these spline generated SISs can be achieved by the samples of Radon transform at almost every angle. Numerical simulation is conducted to check the result

Framelet perturbation and application to nouniform sampling approximation for Sobolev space

The Sobolev space $H^{s}(\mathbb{R}^{d})$, where $s > d/2$, is an important function space that has many applications in various areas of research. Attributed to the inertia of a measuring instrument, it is desirable in sampling theory to reconstruct a function by its nonuniform samples. In the present paper, we investigate the problem of constructing the approximation to all the functions in $H^{s}(\mathbb{R}^{d})$ with nonuniform samples by utilizing dual framelet systems for the Sobolev space pair $(H^{s}(\mathbb{R}^{d}), H^{-s}(\mathbb{R}^{d}))$. We first establish the convergence rates of the framelet series in $(H^{s}(\mathbb{R}^{d}), H^{-s}(\mathbb{R}^{d}))$, and then construct the framelet approximation operator holding for the entire space $H^{s}(\mathbb{R}^{d})$. Using the approximation operator, any function in $H^{s}(\mathbb{R}^{d})$ can be approximated at the exponential rate with respect to the scale level. We examine the stability property for the perturbations of the framelet approximation operator with respect to shift parameters, and obtain an estimate bound for the perturbation error. Our result shows that under the condition $s > d/2$, the approximation operator is robust to the shift perturbation. These results are used to establish the nonuniform sampling approximation for every function in $H^{s}(\mathbb{R}^{d})$. In particular, the new nonuniform sampling approximation error is robust to the jittering of the samples

Nonuniform sampling and approximation in Sobolev space from the perturbation of framelet system

The Sobolev space $H^{\varsigma}(\mathbb{R}^{d})$, where $\varsigma > d/2$, is an important function space that has many applications in various areas of research. Attributed to the inertia of a measurement instrument, it is desirable in sampling theory to recover a function by its nonuniform sampling. In the present paper, based on dual framelet systems for the Sobolev space pair $(H^{s}(\mathbb{R}^{d}), H^{-s}(\mathbb{R}^{d}))$, where $d/2<s<\varsigma$, we investigate the problem of constructing the approximations to all the functions in $H^{\varsigma}(\mathbb{R}^{d})$ by nonuniform sampling. We first establish the convergence rate of the framelet series in $(H^{s}(\mathbb{R}^{d}), H^{-s}(\mathbb{R}^{d}))$, and then construct the framelet approximation operator that acts on the entire space $H^{\varsigma}(\mathbb{R}^{d})$. We examine the stability property for the framelet approximation operator with respect to the perturbations of shift parameters, and obtain an estimate bound for the perturbation error. Our result shows that under the condition $d/2<s<\varsigma$, the approximation operator is robust to shift perturbations. Motivated by some recent work on nonuniform sampling and approximation in Sobolev space (e.g., [20]), we don't require the perturbation sequence to be in $\ell^{\alpha}(\mathbb{Z}^{d})$. Our results allow us to establish the approximation for every function in $H^{\varsigma}(\mathbb{R}^{d})$ by nonuniform sampling. In particular, the approximation error is robust to the jittering of the samples.Comment: arXiv admin note: text overlap with arXiv:1707.0132