• DocumentCode
    2715525
  • Title

    Millimeter Wave Image Restoration Using a Modified T-S Fuzzy Neural Network

  • Author

    Shang, Li ; Su, Pingang ; Huai, Wenjun ; Sun, Zhanli

  • Author_Institution
    Dept. of Electron. Inf. Eng., Suzhou Vocational Univ., Suzhou, China
  • fYear
    2012
  • fDate
    11-13 Aug. 2012
  • Firstpage
    213
  • Lastpage
    216
  • Abstract
    In order to reduce the much unknown noise and improve the resolution of images acquired by millimeter wave imaging system, and combined the advantages of fuzzy theory and neural network, a new MMW image restoration method using a modified Takagi-Sugeno (T-S) fuzzy neural network model is proposed in this paper. The modified T-S fuzzy neural network has the excellent ability of adaptive learning, nonlinear expression and pattern classification. Utilizing this T-S fuzzy neural network model, it is needless to know the degraded model of a MMW image in advance and the noise in MMW images can be detected efficiently. Using the single noise ratio (SNR) to measure the quality of restoration images, simulation results show that the T-S fuzzy neural network can restore the MMW image satisfactorily.
  • Keywords
    fuzzy neural nets; image classification; image restoration; learning (artificial intelligence); millimetre wave imaging; MMW image restoration method; SNR; adaptive learning; millimeter wave image restoration; millimeter wave imaging system; modified Takagi-Sugeno fuzzy neural network model; nonlinear expression; pattern classification; quality measurement; single noise ratio; unknown noise reduction; Educational institutions; Filtering; Fuzzy neural networks; Image restoration; Imaging; Neural networks; Noise; Image restoration; T-S model; degraded image; fuzzy neural network; millimeter wave;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science & Service System (CSSS), 2012 International Conference on
  • Conference_Location
    Nanjing
  • Print_ISBN
    978-1-4673-0721-5
  • Type

    conf

  • DOI
    10.1109/CSSS.2012.61
  • Filename
    6394300