We provide conditions for exact reconstruction of a bandlimited function from irregular polar samples of its Radon transform. First, we prove that the Radon transform is a continuous L2-operator for certain classes of bandlimited signals. We then show that the Beurling-Malliavin condition for the radial sampling density ensures existence and uniqueness of a solution. Moreover, Jaffard's density condition is sufficient for stable reconstruction.
Bandlimitedness conditions also appear implicitly in reconstruction techniques based on discretizations of the inverse Radon transform. Filtered backprojection tacitly assumes that the Radon transform is bandlimited and periodic in the radial coordinate (for computation of a so-called absolute derivative operator) and that quadrature rules for the angular integral (for the backprojection) are exact---for example by assuming that the angular component of the Radon transform has a finite Fourier series representation @cite_14 .
@cite_14
2059596616 2133489575 2092543127 1642187803
The Radon transform of a bivariate function, which has application in tomographic imaging, has traditionally been viewed as a parametrized univariate function. In this paper, the Radon transform is instead viewed as a bivariate function and two-dimensional sampling theory is used to address sampling and information content issues. It is Shown that the band region of the Radon transform of a function with a finite space-bandwidth product is a "finite-length bowtie." Because of the special shape of this band region. "Nyquist sampling" of the Radon transform is on a hexagonal grid. This sampling grid requires approximately one-half as many samples as the rectangular grid obtained from the traditional viewpoint. It is also shown that for a nonbandlimited function of finite spatial support, the bandregion of the Radon transform is an "infinite-length bowtie." Consequently, it follows that approximately 2M2 π independent pieces of information about the function can be extracted from M "projections." These results and others follow very naturally from the two-dimensional viewpoint presented. Distributed sampling and reconstruction of a physical field using an array of sensors is a problem of considerable interest in environmental monitoring applications of sensor networks. Our recent work has focused on the sampling of bandlimited sensor fields. However, sensor fields are not perfectly bandlimited but typically have rapidly decaying spectra. In a classical sampling set-up it is possible to precede the A D sampling operation with an appropriate analog anti-aliasing filter. However, in the case of sensor networks, this is infeasible since sampling must precede filtering. We show that even though the effects of aliasing on the reconstruction cannot be prevented due to the "filter-less" sampling constraint, they can be suitably controlled by oversampling and carefully reconstructing the field from the samples. We show using a dither-based scheme that it is possible to estimate non-bandlimited fields with a precision that depends on how fast the spectral content of the field decays. We develop a framework for analyzing non-bandlimited fields that lead to upper bounds on the maximum pointwise error for a spatial bit rate of R bits meter. We present results for fields with exponentially decaying spectra as an illustration. In particular, we show that for fields f(t) with exponential tails; i.e., F( spl omega ) < spl pi spl alpha sup - spl alpha | spl omega | , the maximum pointwise error decays as c2e sup -a sub 1 spl radic R+ c3 1 spl radic R (e sup -2a sub 1 spl radic R) with spatial bit rate R bits meter. Finally, we show that for fields with spectra that have a finite second moment, the distortion decreases as O((1 N) sup 2 3 ) as the density of sensors, N, scales up to infinity . We show that if D is the targeted non-zero distortion, then the required (finite) rate R scales as O (1 spl radic D log 1 D). We describe the wavelet–vaguelette decomposition (WVD) of a linear inverse problem. It is a substitute for the singular value decomposition (SVD) of an inverse problem, and it exists for a class of special inverse problems of homogeneous type—such as numerical differentiation, inversion of Abel-type transforms, certain convolution transforms, and the Radon transform. We propose to solve ill-posed linear inverse problems by nonlinearly "shrinking" the WVD coefficients of the noisy, indirect data. Our approach offers significant advantages over traditional SVD inversion in recovering spatially inhomogeneous objects. We suppose that observations are contaminated by white noise and that the object is an unknown element of a Besov space. We prove that nonlinear WVD shrinkage can be tuned to attain the minimax rate of convergence, for L2 loss, over the entire scale of Besov spaces. The important case of Besov spaces Bσp,q, p < 2, which model spatial inhomogeneity, is included. In comparison, linear procedures— SVD included—cannot attain optimal rates of convergence over such classes in the case p < 2. For example, our methods achieve faster rates of convergence for objects known to lie in the bump algebra or in bounded variation than any linear procedure. In image reconstruction there are techniques that use analytical formulae for the Radon transform to recover an image from a continuum of data. In practice, however, one has only discrete data available. Thus one often resorts to sampling and interpolation methods. This article presents an approach to the inversion of the Radon transform that uses a discrete set of samples which need not be completely regular.