Image-to-image translation for improved digital holographic reconstruction based on a generative adversarial network learning framework

Lu, Zhenzhong; Cao, Yanlong; Liu, Min; Han, Bo; Liao, Jiali; Sun, Yanjun; Ma, Lin

doi:10.1016/j.optlastec.2023.109654

Cited by 4 publications

(2 citation statements)

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“…CNNs can be used for hologram generation [20][21][22] and reconstruction, including noise, twin image and zero-order suppression [23]. CNNs are typically used to reconstruct one image [24][25][26][27][28][29][30][31][32] or two (amplitude and phase information) [33][34][35][36][37][38][39][40][41] or extended focus imaging [41,42]. However, the direct reconstruction of the entire 3D-scene provides a wider range of possibilities [43].…”

Section: Introductionmentioning

confidence: 99%

“…GANs are now widely used for a variety problems [45][46][47], as they allow realistic images to be created and processed [48]. Modifications of such methods are widely used in digital optics, including already existing methods hologram reconstruction aimed at obtaining a single image [25][26][27][28][29][30] or two images (amplitude and phase) [40].…”

Section: Introductionmentioning

confidence: 99%

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Generative adversarial neural network for 3D-hologram reconstruction

Kiriy,

Rymov,

Svistunov

et al. 2024

Laser Phys. Lett.

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Neural-network-based reconstruction of digital holograms can improve the speed and the quality of micro- and macro-object images, as well as reduce the noise and suppress the twin image and the zero-order. Usually, such methods aim to reconstruct the 2D object image or amplitude and phase distribution. In this paper, we investigated the feasibility of using a generative adversarial neural network to reconstruct 3D-scenes consisting of a set of cross-sections. The method was tested on computer-generated and optically-registered digital inline holograms. It enabled the reconstruction of all layers of a scene from each hologram. The reconstruction quality is improved 1.8 times when compared to the U-Net architecture on the normalized standard deviation value.

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