Pixel-wise phase unwrapping of fringe projection profilometry based on deep learning

Abstract

Phase unwrapping is a crucial step in high-precision optical metrology, aimed at retrieving absolute phases from wrapped phases. This paper presents a deep learning-based phase unwrapping method that enhances the conventional three-wavelength heterodyne method and achieves pixel-wise phase unwrapping using a wrapped phase map and two fringe patterns. The proposed method formulates the phase unwrapping task as a semantic segmentation problem that infers an absolute fringe order for each wrapped phase pixel. The verification results demonstrate that the method can accurately measure surfaces with complex topologies. Under low noise conditions, the proposed method achieves performance comparable to the three-wavelength heterodyne method, with similarity rates between the fringe order maps obtained by the two methods of up to 0.99; additionally, the method exhibits superb resistance to severe noise. Moreover, the proposed method is more efficient in terms of fringe pattern efficiency by at least 44.44%.