Given a blurred image g(x, y), variational blind deconvolution seeks to reconstruct both the unknown blur k(x, y) and the unknown sharp image f (x, y), by minimizing an appropriate cost functional. This paper restricts its attention to a rich and significant class of infinitely divisible isotropic blurs that includes Gaussians, Lorentzians, and other heavy-tailed densities, together with their convolutions. A recently developed highly efficient nonlinear variational approach is found to produce inadmissible reconstructions, consisting of partially deblurred images f † (x, y), associated with physically impossible blurs k † (x, y). Three basic flaws in this variational procedure are identified and shown to be the cause of this phenomenon. A method is then developed that can recover useful information from k † (x, y), by constructing a physically valid rectified blur h # (x, y), based on the low frequency part of k † (x, y). A crucial step involves interpreting h # (x, y) as the p th convolution root of the true blur k(x, y), for some postulated real number p ≥ 2. Deconvolution is performed in slow motion, by solving an associated parabolic pseudo-differential equation backwards in time, with the blurred image g(x, y) as data at t = 1. Behavior of the evolution as t ↓ 0 can be monitored and used to readjust the value of p. Previously developed APEX/SECB methodologies make such ill-posed continuation feasible. This recovery method is found highly effective in several instructive examples involving synthetically blurred images.