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Generating Multi-Depth 3D Holograms Using a Fully Convolutional Neural Network.
Yan, Xingpeng; Liu, Xinlei; Li, Jiaqi; Zhang, Yanan; Chang, Hebin; Jing, Tao; Hu, Hairong; Qu, Qiang; Wang, Xi; Jiang, Xiaoyu.
Afiliación
  • Yan X; Department of Information Communication, Army Academy of Armored Forces, Beijing, 100072, China.
  • Liu X; Department of Information Communication, Army Academy of Armored Forces, Beijing, 100072, China.
  • Li J; National Digital Switching System Engineering and Technological Research Center, Zhengzhou, 450001, China.
  • Zhang Y; Information Engineering University, Zhengzhou, 450001, China.
  • Chang H; Department of Information Communication, Army Academy of Armored Forces, Beijing, 100072, China.
  • Jing T; Department of Information Communication, Army Academy of Armored Forces, Beijing, 100072, China.
  • Hu H; Department of Information Communication, Army Academy of Armored Forces, Beijing, 100072, China.
  • Qu Q; Department of Information Communication, Army Academy of Armored Forces, Beijing, 100072, China.
  • Wang X; Department of Information Communication, Army Academy of Armored Forces, Beijing, 100072, China.
  • Jiang X; Department of Information Communication, Army Academy of Armored Forces, Beijing, 100072, China.
Adv Sci (Weinh) ; 11(28): e2308886, 2024 Jul.
Article en En | MEDLINE | ID: mdl-38725135
ABSTRACT
Efficiently generating 3D holograms is one of the most challenging research topics in the field of holography. This work introduces a method for generating multi-depth phase-only holograms using a fully convolutional neural network (FCN). The method primarily involves a forward-backward-diffraction framework to compute multi-depth diffraction fields, along with a layer-by-layer replacement method (L2RM) to handle occlusion relationships. The diffraction fields computed by the former are fed into the carefully designed FCN, which leverages its powerful non-linear fitting capability to generate multi-depth holograms of 3D scenes. The latter can smooth the boundaries of different layers in scene reconstruction by complementing information of occluded objects, thus enhancing the reconstruction quality of holograms. The proposed method can generate a multi-depth 3D hologram with a PSNR of 31.8 dB in just 90 ms for a resolution of 2160 × 3840 on the NVIDIA Tesla A100 40G tensor core GPU. Additionally, numerical and experimental results indicate that the generated holograms accurately reconstruct clear 3D scenes with correct occlusion relationships and provide excellent depth focusing.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Adv Sci (Weinh) Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Alemania
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Adv Sci (Weinh) Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Alemania