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DenSFA-PU: Learning to unwrap phase in severe noisy conditions

Year: 2025
Type: article
Abstract: In optics, phase measurement techniques face challenges because phase values are confined within 2π, leading to the problem of phase unwrapping. Many methods, including deep learning-based approaches, have been proposed to address this issue. However, high noise in a wrapped phase image often causes these techniques to fail, resulting in error accumulation and high computation time. To overcome these challenges, we propose a robust and fast deep learning-based method called DenSFA-PU ( Den sely Connected S patial F eature A ggregator for P hase U nwrapping), which treats this problem as a regression task. Our approach uses an encoder-decoder architecture with densely connected neural networks and a spatial feature aggregator module for noise reduction and robust feature representation. Comparative analysis using both synthetic data and real-world data obtained through digital holography demonstrates that our method outperforms existing techniques, achieving greater computational efficiency with an average unwrapping time of 29.31 ms, significantly faster than other methods. It also shows superior accuracy, with consistently good NRMSE, PSNR, and SSIM values across all cases, highlighting its robustness in handling highly noisy wrapped phase images. Additionally, its ability to operate with minimal training data makes it highly suitable for the applications requiring fast and accurate phase unwrapping with a limited data set.
DOI: 10.1016/j.optlastec.2025.112757
Authors: Muhammad Awais, Taeil Yoon, Chi-Ok Hwang, Byeongha Lee
Language: English
Open Access status: hybrid
Estimated APC paid: $3,230
OpenAlex ID: W4408356877
FWCI: 3.247
Cites: 49
Cited by: 9
Related to: 10