The Funk-Radon transform assigns to a function on the two-sphere its mean values along all great circles. We consider the following generalization: we replace the great circles by the small circles being the intersection of the sphere with planes containing a common point ζ inside the sphere. If ζ is the origin, this is just the classical Funk-Radon transform. We find two mappings from the sphere to itself that enable us to represent the generalized Radon transform in terms of the Funk-Radon transform. This representation is utilized to characterize the nullspace and range as well as to prove an inversion formula of the generalized Radon transform.
This paper deals with the inversion of the spherical Funk-Radon transform, and, more generally, with the inversion of spherical convolution operators from the point of view of statistical inverse problems. This means we consider discrete data perturbed by white noise and aim at estimators with optimal mean square error for functions out of a Sobolev ball. To this end we analyze a specific class of estimators built upon the spherical hyperinterpolation operator, spherical designs and the mollifier approach. Eventually, we determine optimal mollifier functions with respect to the noise level, the number of data points and the smoothness of the original function. We complete this paper by providing a fast algorithm for the numerical computation of the estimator, which is based on the fast spherical Fourier transform, and by illustrating our theoretical results with numerical experiments.
We present a novel algorithm for the inversion of the vertical slice transform, i.e. the transform that associates to a function on the twodimensional unit sphere all integrals along circles that are parallel to one fixed direction. Our approach makes use of the singular value decomposition and resembles the mollifier approach by applying numerical integration with a reconstruction kernel via a quadrature rule. Considering the inversion problem as a statistical inverse problem, we find a family of asymptotically optimal mollifiers that minimize the maximum risk of the mean integrated error for functions within a Sobolev ball. By using fast spherical Fourier transforms and the fast Legendre transform, our algorithm can be implemented with almost linear complexity. In numerical experiments, we compare our algorithm with other approaches and illustrate our theoretical findings.
Inverse Problems 37 (2021) 115002 C Kirisits et al a Fourier diffraction theorem and derive novel backpropagation formulae for the reconstruction of the scattering potential, which depends on the refractive index distribution inside the object, taking its complicated motion into account. This provides the basis for solving the ODT problem with an efficient non-uniform discrete Fourier transform.
We consider the imaging problem of the reconstruction of a three-dimensional object via optical diffraction tomography under the assumptions of the Born approximation. Our focus lies in the situation that a rigid object performs an irregular, time-dependent rotation under acoustical or optical forces. In this study, we compare reconstruction algorithm in case i) that two-dimensional images of the complex-valued wave are known, or ii) that only the intensity (absolute value) of these images can be measured, which is the case in many practical setups. The latter phase-retrieval problem can be solved by an all-at-once approach based utilizing a hybrid input-output scheme with TV regularization.
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