Robust phase retrieval with green noise binary masks

• Published in: Optics and lasers in engineering Vol. 149; p. 106808
• Main Authors: Ye, Qiuliang, Chan, Yuk-Hee, Somekh, Michael G., Lun, Daniel P.K.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2022
• Subjects: Coded diffraction patterns; Green noise mask; Non-bandlimited noise; Phase retrieval; Phase retrieval; Green noise mask; Coded diffraction patterns; Non-bandlimited noise
• ISSN: 0143-8166; 1873-0302
• DOI: 10.1016/j.optlaseng.2021.106808

•Coded phase retrieval methods use random masks to generate diffraction patterns.•The non-bandlimited problem of the traditional white noise masks is often overlooked.•Green noise masks have energy concentrated at the mid-frequency region.•Green noise masks outperform the traditional white noise masks in phase retrieval. Phase retrieval with pre-defined optical masks can provide extra constraints and thus achieve improved performance. Recent progress in optimization theory demonstrates the superiority of random masks in enhancing the accuracy of phase retrieval algorithms. However, traditional approaches only focus on the randomness of the masks but ignore their non-bandlimited nature. When using these masks for phase retrieval, the intensity measurements contain many significant high-frequency components that the phase retrieval algorithm cannot take care of and thus leads to degraded performance. Based on the concept of digital halftoning, this paper proposes a green noise binary masking scheme that can significantly reduce the high-frequency contents of the masks while fulfilling the randomness requirement. The resulting intensity measurements will contain data concentrated in the mid-frequency band and around zero frequency areas which can be fully utilized in the phase retrieval optimization process. Our experimental results show that the proposed green noise binary masking scheme consistently outperforms the traditional ones when using in binary coded diffraction pattern phase retrieval systems.