One-step robust deep learning phase unwrapping.

By Kaiqiang Wang, Ying Li, Qian Kemao, Jianglei Di, Jianlin Zhao. Optics Express, 2019

319 citations recorded by the source corpus.

Phase unwrapping is an important but challenging issue in phase measurement. Even with the research efforts of a few decades, unfortunately, the problem remains not well solved, especially when heavy noise and aliasing (undersampling) are present. We propose a database generation method for phase-type objects and a one-step deep learning phase unwrapping method. With a trained deep neural network, the unseen phase fields of living mouse osteoblasts and dynamic candle flame are successfully unwrapped, demonstrating that the complicated nonlinear phase unwrapping task can be directly fulfilled in one step by a single deep neural network. Excellent anti-noise and anti-aliasing performances outperforming classical methods are highlighted in this paper.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Radiation

View Paper: https://www.semanticscholar.org/paper/945d95aa165199d8e0cebbedbc1ddffd8f474a9f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Chao Zuo
2. Yixuan Li
3. Kemao Qian
4. Pengfei Fan
5. Wei Yin
6. Jing Han
7. Shijie Feng
8. Qian Chen

In Media Technology

1. Chao Zuo
2. Wei Yin
3. Shijie Feng
4. Qian Chen
5. Song Zhang
6. Bing Pan

In Radiation

1. Chao Zuo
2. Yixuan Li
3. Kemao Qian
4. Pengfei Fan
5. Wei Yin
6. Jing Han
7. Shijie Feng
8. Qian Chen

Citations in the corpus

• Deep learning in optical metrology: a review (2022, methodology): “Temporal phase retrieval: Wang et al.312 introduced a deep-learning scheme to the phase-shifting technique in FPP.”