A novel approach for regularized inversion of noisy Laplace transforms using real-axis data points

This paper presents a new approach to construct regularizing operators for the inversion of noisy Laplace transforms known as a set of data points on the real axis. The effectiveness of the proposed approach is demonstrated through examples of noisy Laplace transform inversions and the deconvolution of nuclear magnetic resonance relaxation data, including experimentally measured data. The software implementation of this method allows for enforcing the positivity of the solution without requiring any additional information.