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An Enhancement Method of Document Restoration Capability using Encryption and DnCNN

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2022, 8(2), pp.79-84
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : February 8, 2022
  • Accepted : March 21, 2022
  • Published : April 30, 2022

Hyun-Hee Jang 1 Ha,Sung-Jae 2 Cho, Gi Hwan 3

1한국폴리텍V대학
2한국폴리텍 IV 대학 대전캠퍼스
3전북대학교

Accredited

ABSTRACT

This paper presents an enhancement method of document restoration capability which is robust for security, loss, and contamination, It is based on two methods, that is, encryption and DnCNN(DeNoise Convolution Neural Network). In order to implement this encryption method, a mathematical model is applied as a spatial frequency transfer function used in optics of 2D image information. Then a method is proposed with optical interference patterns as encryption using spatial frequency transfer functions and using mathematical variables of spatial frequency transfer functions as ciphers. In addition, by applying the DnCNN method which is bsed on deep learning technique, the restoration capability is enhanced by removing noise. With an experimental evaluation, with 65% information loss, by applying Pre-Training DnCNN Deep Learning, the peak signal-to-noise ratio (PSNR) shows 11% or more superior in compared to that of the spatial frequency transfer function only. In addition, it is confirmed that the characteristic of CC(Correlation Coefficient) is enhanced by 16% or more.

Citation status

* References for papers published after 2022 are currently being built.