Technical Report: Improved Fourier Reconstruction using Jump Information   with Applications to MRI
  Certain applications such as Magnetic Resonance Imaging (MRI) require the reconstruction of functions from Fourier spectral data. When the underlying functions are piecewise-smooth, standard Fourier approximation methods suffer from the Gibbs phenomenon - with associated oscillatory artifacts in the vicinity of edges and an overall reduced order of convergence in the approximation. This paper proposes an edge-augmented Fourier reconstruction procedure which uses only the first few Fourier coefficients of an underlying piecewise-smooth function to accurately estimate jump information and then incorporate it into a Fourier partial sum approximation. We provide both theoretical and empirical results showing the improved accuracy of the proposed method, as well as comparisons demonstrating superior performance over existing state-of-the-art sparse optimization-based methods. Extensions of the proposed techniques to functions of several variables are also addressed preliminarily. All code used to generate the results in this report are made publicly available. 
Jade Larriva-Latt, Angela Morrison, Alison Radgowski, Joseph Tobin,   Aditya Viswanathan, and Mark Iwen
cs.NA