频谱卷积神经网络实现全息图散斑降噪

Translated title of the contribution: Speckle Noise Reduction of Holograms Based on Spectral Convolutional Neural Network

Wenjing Zhou, Shuai Zou, Dengke He, Jinglu Hu, Yingjie Yu

Research output: Contribution to journalArticle

Abstract

Digital holographic system is a promising image-forming system, but speckle noise in the coherent light source of digital holographic system adversely affects the quality of holograms. There are some disadvantages in conventional experimental noise reduction or traditional neural network-based noise reduction methods. In order to realize speckle noise reduction in holograms and balance the efficiency of noise reduction, a fast noise reduction algorithm based on convolutional neural network for single hologram is proposed, and the speckle noise dataset is used to train multilevel neural networks. Theoretical analysis and experimental results show that the convolution neural network applied in digital hologram spectrum domain denoising can effectively improve the quality of the hologram, and multilevel speckle noise can be effectively processed by only one hologram. which can save the effective interference fringes of holograms to the maximum extent while maintaining the denoising performance.

Translated title of the contributionSpeckle Noise Reduction of Holograms Based on Spectral Convolutional Neural Network
Original languageChinese
Article number0509001
JournalGuangxue Xuebao/Acta Optica Sinica
Volume40
Issue number5
DOIs
Publication statusPublished - 2020 Mar 10

Keywords

  • Digital hologram
  • Neural network
  • Speckle noise
  • Spectral noise reduction

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics

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