Michel Defrise

TL;DR: It is proved that replacing the usual quadratic regularizing penalties by weighted 𝓁p‐penalized penalties on the coefficients of such expansions, with 1 ≤ p ≤ 2, still regularizes the problem.

Abstract: We consider linear inverse problems where the solution is assumed to have a sparse expansion on an arbitrary pre-assigned orthonormal basis. We prove that replacing the usual quadratic regularizing penalties by weighted l^p-penalties on the coefficients of such expansions, with 1 < or = p < or =2, still regularizes the problem. If p < 2, regularized solutions of such l^p-penalized problems will have sparser expansions, with respect to the basis under consideration. To compute the corresponding regularized solutions we propose an iterative algorithm that amounts to a Landweber iteration with thresholding (or nonlinear shrinkage) applied at each iteration step. We prove that this algorithm converges in norm. We also review some potential applications of this method.

TL;DR: This paper presents two new rebinning algorithms for the reconstruction of three-dimensional (3-D) positron emission tomography (PET) data that are approximate but allows an efficient implementation based on taking 2-D Fourier transforms of the data.

TL;DR: A new method to match a 2D image to a translated, rotated and scaled reference image using symmetric phase-only matched filtering to the FMI descriptors, which guarantees high discriminating power and excellent robustness in the presence of noise.

TL;DR: Iterative reconstruction algorithms for helical CT are presented, derived from two-dimensional reconstruction algorithms, by adapting the projector/backprojector to the helical orbit of the source, and by constraining the axial frequencies with a Gaussian sieve.