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A Novel Noise Removal Method Using Neural Networks

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  • Catalina Lucia COCIANU
  • Alexandru Daniel STAN

Abstract

In this paper is presented a new technique consisting in applying some pre-whitening and shrinkage methods followed by a neural network-based supervised approach for correlated noise removal purposed. In our work the type of noise and the covariance matrix of noise are known or can be estimated using the “white wall†method. Due to data dimensionality, a PCA-based compression technique is used to obtain a tractable solution. The local memories of the neurons are determined using a supervised learning process based on the compressed pre-processed inputs and the compressed version of the original images. The proposed method is evaluated using some of the most commonly used indicators and the results are reported in the third section of the paper. The conclusive remarks together with suggestions for further work are supplied in the final part of the paper.

Suggested Citation

  • Catalina Lucia COCIANU & Alexandru Daniel STAN, 2016. "A Novel Noise Removal Method Using Neural Networks," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 20(3), pages 66-75.
    • Handle: RePEc:aes:infoec:v:20:y:2016:i:3:p:66-75
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    References listed on IDEAS

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    1. Iain M. Johnstone & Bernard W. Silverman, 1997. "Wavelet Threshold Estimators for Data with Correlated Noise," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(2), pages 319-351.
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