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27 November 2023 Speckle denoising of digital holographic interferometry via integrated CycleGAN
Hongbo Yu, Qiang Fang, Qinghe Song, Jiayi Song, Haiting Xia
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Proceedings Volume 12768, Holography, Diffractive Optics, and Applications XIII; 1276825 (2023) https://doi.org/10.1117/12.2687350
Event: SPIE/COS Photonics Asia, 2023, Beijing, China
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Abstract
Digital Holographic Interferometry (DHI) provides a non-contact measurement at the wavelength level of light. Due to the application of the interferometric principle, non-Gaussian speckle noise introduced during the measurement process is unavoidable and is difficult to eliminate. Thus, denoising is critical and affects measurement accuracy. A method for speckle denoising via self-supervised deep learning, based on a Cycle-Generative Adversarial Network (CycleGAN), is proposed in this paper. The method employs unpaired datasets and integrates a 4-f optical speckle noise simulation module to reduce training costs while improving training accuracy. The proposed method was tested on both simulated and experimental data, with results showing a 4.6% performance improvement in PSNR over competitor algorithms. The proposed method has great potential and advantages in DHI studies with huge datasets.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Hongbo Yu, Qiang Fang, Qinghe Song, Jiayi Song, and Haiting Xia "Speckle denoising of digital holographic interferometry via integrated CycleGAN", Proc. SPIE 12768, Holography, Diffractive Optics, and Applications XIII, 1276825 (27 November 2023); https://doi.org/10.1117/12.2687350
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KEYWORDS
Speckle
Denoising
Deep learning
Holographic interferometry
Digital holography
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