Unsupervised speckle denoising in digital holographic interferometry based on 4-f optical simulation integrated cycle-consistent generative adversarial network

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 spe...

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Veröffentlicht in:	Applied optics (2004) 2024-05, Vol.63 (13), p.3557-3569
Hauptverfasser:	Yu, HongBo, Fang, Qiang, Song, QingHe, Montresor, Silvio, Picart, Pascal, Xia, Haiting
Format:	Artikel
Sprache:	eng
Schlagworte:	Datasets Deep learning Generative adversarial networks Holographic interferometry Noise reduction Signal to noise ratio Simulation
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