A Multi-Task Learning for 2D Phase Unwrapping in Fringe Projection

Abstract

Phase unwrapping is a challenging task in signal processing, spanning its applications in optical metrology, SAR interferometry, and many other signal reconstruction tasks. Fringe Projection Profilometry is a popular active-sensing approach for generating high-resolution three-dimensional (3D) surface information in which phase unwrapping is a crucial step. This letter proposes a multi-task learning-based phase unwrapping method for simultaneous denoising and wrap-count prediction in fringe projection. The proposed network, referred to as TriNet, has nested pyramidal architecture with a single encoder and two decoders, all connected through skip connections. The proposed approach does not require any pre-processing for noise removal like the conventional methods or any post-processing such as smoothing, like in existing deep learning methods but results in a quite accurate phase unwrapping. The proposed method outperforms the existing and state-of-the-art methods for the 3D reconstruction task in Fringe Projection by a significant margin even in the presence of very high noise.