Phase unwrapping in optical metrology via denoised and convolutional segmentation networks

TLDR: A new approach is proposed, where the task of phase unwrapping is transferred into a multi-class classification problem and an efficient segmentation network is introduced to identify classes and a noise-to-noise denoised network is integrated to preprocess noisy wrapped phase.

Abstract: The interferometry technique is commonly used to obtain the phase information of an object in optical metrology. The obtained wrapped phase is subject to a 2π ambiguity. To remove the ambiguity and obtain the correct phase, phase unwrapping is essential. Conventional phase unwrapping approaches are time-consuming and noise sensitive. To address those issues, we propose a new approach, where we transfer the task of phase unwrapping into a multi-class classification problem and introduce an efficient segmentation network to identify classes. Moreover, a noise-to-noise denoised network is integrated to preprocess noisy wrapped phase. We have demonstrated the proposed method with simulated data and in a real interferometric system.