NEW REGULARIZATION SCHEME FOR PHASE UNWRAPPING

A new, to our knowledge, algorithm for the phase unwrapping (PU) probl em that is based on stochastic relaxation is proposed and analyzed. Un like regularization schemes previously proposed to handle this problem , our approach dispells the following two assumptions about the soluti on: a Gaussian model for noise and the magnitude of the true phase-fie ld gradient's being less than pi everywhere. We formulate PU as a cons trained optimization problem for the field of integer multiples of 2 p i, which must be added to the wrapped phase gradient to recover the tr ue phase gradient. By solving the optimization problem using simulated annealing with constraints, one can obtain a consistent solution unde r difficult conditions resulting from noise and undersampling. Results from synthetic test images are reported. (C) 1998 Optical Society of America.