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Unsupervised speckle denoising in digital holographic interferometry based on 4-f optical simulation integrated CycleGAN

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posted on 2024-02-16, 09:09 authored by Hai-Ting Xia, HongBo Yu, Fang Qiang, qinghe song, Silvio Montresor, Pascal Picart
The speckle noise generated during digital holographic interferometry (DHI) is unavoidable and difficult to eliminate, thus reducing its accuracy. We propose a self-supervised deep learning speckle denoising method using a cycle-consistent generative adversarial network to mitigate the effect of speckle noise. The proposed method integrates a 4-f optical speckle noise simulation module with a parameter generator. In addition, it uses an unpaired dataset for training to overcome the difficulty in obtaining noise-free images and paired data from experiments. The proposed method was tested on both simulated and experimental data, with results showing a 6.9% performance improvement compared with a conventional method and a 2.6% performance improvement compared with unsupervised deep learning in terms of the peak signal-to-noise ratio. Thus, the proposed method exhibits superior denoising performance and potential for DHI, being particularly suitable for processing large datasets.

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Funder Name

National Natural Science Foundation of China (11862008,62165007)

Preprint ID

111836

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