The numerical computation of ill-posed, time-reversed, nonlinear parabolic equations presents considerable difficulties. Conventional stepwise marching schemes for such problems, whether explicit or implicit, are necessarily unconditionally unstable, and result in explosive noise amplification. This paper develops a method for stabilizing the explicit, accurate, centred time differencing leapfrog scheme, introduced by L. F. Richardson almost 100 years ago, and now widely used in meteorology and oceanography. The method uses FFT-synthesized linear smoothing operators at each time step, based on positive real powers of the negative Laplacian, to quench the instability. This smoothing operation leads to a distortion away from the true solution. This is the stabilization penalty. It is shown that in many problems of interest, that distortion is often small enough to allow for useful results. In the canonical case of linear autonomous selfadjoint time-reversed parabolic equations, with solutions satisfying prescribed bounds, it is proved that the stabilized leapfrog scheme leads to an error estimate differing from the best-possible estimate, only by the stabilization penalty. The stabilized parabolic leapfrog scheme is linearly stable, marching forward or backward in time. However, as in linearly stable leapfrog meteorological wave propagation schemes, application to nonlinear parabolic problems leads to computational instability that must be eliminated using Robert–Asselin–Williams filtering. Instructive computational examples are provided of FFT-Laplacian stabilized ill-posed reconstructions, both in rectangular and non-rectangular regions. These examples involve 8 bit gray scale images fictitiously blurred by nonlinear parabolic equations, and highlight the feasibility, or infeasibility, of backward in time continuation in such equations.
Read full abstract