Noise2Noise self-supervised deep learning holographic despeckling method

Chenghua Shen; Wenjing Zhou; Hongbo Zhang; Yingjie Yu

doi:10.1117/12.2641871

19 December 2022 Noise2Noise self-supervised deep learning holographic despeckling method

Chenghua Shen, Wenjing Zhou, Hongbo Zhang, Yingjie Yu

Author Affiliations +

Proceedings Volume 12318, Holography, Diffractive Optics, and Applications XII; 123180D (2022) https://doi.org/10.1117/12.2641871
Event: SPIE/COS Photonics Asia, 2022, Online Only

Abstract

Digital holographic microscopy (DHM) is a non-contact and high accuracy measurement technique widely used in biomedicine, microstructure,and other fields.The quality of the reconstructed image and the effectiveness of holographic microscopy were easily affected by speckle noise. Inspired by the idea of Noise2Noise, we propose a self-supervised noise2noise hologram speckle noise removal method. From the holograms that need denoising to generate the input and labels with the same noise distribution to form a training pair for training. Solve the problem that clean holograms are difficult to obtain.The training sets of this self-supervised method are generated from the holograms to be processed. As such, it avoids the need of collectting a large number of training sets. The proposed method is therefore less vulnerable to the background noises and more convenient and reliable for practical hologram speckle denoising applications.

Conference Presentation

Citation Download Citation

Chenghua Shen, Wenjing Zhou, Hongbo Zhang, and Yingjie Yu "Noise2Noise self-supervised deep learning holographic despeckling method", Proc. SPIE 12318, Holography, Diffractive Optics, and Applications XII, 123180D (19 December 2022); https://doi.org/10.1117/12.2641871

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