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

研究成果: Conference contribution

抜粋

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.

元の言語English
ホスト出版物のタイトルHolography, Diffractive Optics, and Applications IX
編集者Yunlong Sheng, Changhe Zhou, Liangcai Cao
出版者SPIE
ISBN(電子版)9781510630932
DOI
出版物ステータスPublished - 2019
イベントHolography, Diffractive Optics, and Applications IX 2019 - Hangzhou, China
継続期間: 2019 10 212019 10 23

出版物シリーズ

名前Proceedings of SPIE - The International Society for Optical Engineering
11188
ISSN(印刷物)0277-786X
ISSN(電子版)1996-756X

Conference

ConferenceHolography, Diffractive Optics, and Applications IX 2019
China
Hangzhou
期間19/10/2119/10/23

ASJC Scopus subject areas

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

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  • これを引用

    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). : Y. Sheng, C. Zhou, & L. Cao (版), Holography, Diffractive Optics, and Applications IX [1118807] (Proceedings of SPIE - The International Society for Optical Engineering; 巻数 11188). SPIE. https://doi.org/10.1117/12.2537685