The joint probability distribution of diffusivity and T2-relaxation coefficient provides useful information to characterize tissue microstructure. A standard approach for estimating this joint distribution relies on estimating the inverse Laplace transform from a large number of measurements which is not only infeasible in clinical settings but also numerically unstable. In this work, we introduce a novel approach, termed REDIM, to probe tissue microstructure using the statistical properties of a family of scaled distribution functions. In particular, we use specific functions to zoom into the joint probability function to robustly estimate different features of the underlying diffusion-relaxation processes. We show that this approach can be reliably implemented for use with in-vivo diffusion MRI (dMRI) data.