Physics

Phase noise

Noise (video)

Fourier transform

Image (mathematics)

Speckle pattern

Optics

Artificial intelligence

Metrology

Interferometry

Speckle noise

Programming language

Computer science

Phase (matter)

Ambiguity

Quantum mechanics

Computer vision

Phase retrieval

Segmentation

Optics Express

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.

Phase unwrapping in optical metrology via denoised and convolutional segmentation networks