An Adaptive Spherical Simplex Radial Cubature Information Filter-Based Phase Unwrapping Method

Abstract

An adaptive spherical simplex radial cubature information filter-based phase unwrapping (ASSRCIFPU) method is introduced. First, the ASSRCIF method with adaptive adjustment of observation noise variance is introduced within the phase unwrapping for interferograms. The ASSRCIFPU program is implemented by integrating a rapid local phase gradient estimator with a heap sort path-following technique. Second, a deep learning-based interferogram fringe boundary detection model is developed to extract fringe boundary information for the interferograms. Subsequently, the fringe boundary information, the pseudocoherence coefficient map and the residue data from the interferogram are combined to produce a reliability mask map that characterizes the phase quality for the interferograms, which categorize the pixels into high-reliability and low-reliability groups based on the phase quality. Finally, the ASSRCIFPU program first unwraps the high-reliability pixel arrays using heap sort path-following technique, followed by unwrapping the remaining wrapped pixels to retrieve the unwrapped phase of the entire interferogram. Experiments on diverse fringe patterns show that this method achieves higher accuracy and efficiency compared to other commonly used methods.