The application provides a fast
phase unwrapping method based on
tail non-convex regularization, and aims at solving the following two technical problems of the existing
phase unwrapping algorithm: one is that details are lost due to the discontinuity of the
image edge; and the other is that it is difficult to accurately realize
phase unwrapping when facing
high complexity or large phase jumps. The steps of the application are as follows: based on the wrapped image, a non-convex
tail optimization model with box constraint is constructed; the non-convex
tail optimization model is solved by using a completely split primal-dual
algorithm under the condition that the support set is empty, so that an
initial phase is obtained; errors in the
initial phase estimation are corrected through tail minimization, and the support set is dynamically updated, so that an unwrapped image is obtained. Compared with the traditional phase unwrapping
algorithm, the application combines the non-convex regularization with the tail minimization strategy, so that the accuracy and robustness of the phase unwrapping are improved, and the application has significant superiority in key evaluation indexes such as
signal-to-
noise ratio and
structural similarity.