Speckle noise reduction in digital holograms based on Spectral Convolutional Neural Networks (SCNN)

Wen Jing Zhou, Shuai Zou, Deng Ke He, Jing Lu Hu, Hong Bo Zhang, Ying Jie Yu, Ting Chung Poon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Digital holographic imaging systems are promising as they provide 3-D information of the object. However, the acquisition of holograms during experiments can be adversely affected by the speckle noise in coherent digital holographic systems. Several different denoising algorithms have been proposed. Traditional denoising algorithms average several holograms under different experimental conditions or use conventional filters to remove the speckle noise. However, these traditional methods require complex holographic experimental conditions. Besides time-consuming, the use of traditional neural networks has been difficult to extract speckle noise characteristics from holograms and the resulting holographic reconstructions have not been ideal. To address tradeoff between speckle noise reduction and efficiency, we analyze holograms in the spectrum domain for fast speckle noise reduction, which can remove multiple-levels speckle noise based on convolutional neural networks using only a single hologram. In order to effectively reduce the speckle noise associated with the hologram, the data set of the neural network training cannot use the current popular image data set. To achieve powerful noise reduction performance, neural networks use multiple-level speckle noise data sets for training. In contrast to existing traditional denoising algorithms, we use convolutional neural networks in spectral denoising for digital hologram. The proposed technique enjoys several desirable properties, including (i) the use of only a single hologram to efficiently handle various speckle noise levels, and (ii) faster speed than traditional approaches without sacrificing denoising performance. Experimental results and holographic reconstruction demonstrate the efficiency of our proposed neural network.

Original languageEnglish
Title of host publicationHolography, Diffractive Optics, and Applications IX
EditorsYunlong Sheng, Changhe Zhou, Liangcai Cao
PublisherSPIE
ISBN (Electronic)9781510630932
DOIs
Publication statusPublished - 2019
EventHolography, Diffractive Optics, and Applications IX 2019 - Hangzhou, China
Duration: 2019 Oct 212019 Oct 23

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11188
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceHolography, Diffractive Optics, and Applications IX 2019
CountryChina
CityHangzhou
Period19/10/2119/10/23

Keywords

  • Digital hologram
  • Neural networks
  • Pattern recognition
  • Spectral noise

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Speckle noise reduction in digital holograms based on Spectral Convolutional Neural Networks (SCNN)'. Together they form a unique fingerprint.

  • Cite this

    Zhou, W. J., Zou, S., He, D. K., Hu, J. L., Zhang, H. B., Yu, Y. J., & Poon, T. C. (2019). Speckle noise reduction in digital holograms based on Spectral Convolutional Neural Networks (SCNN). In Y. Sheng, C. Zhou, & L. Cao (Eds.), Holography, Diffractive Optics, and Applications IX [1118807] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11188). SPIE. https://doi.org/10.1117/12.2537685