Neural Networks for Image Restoration from the Magnitude of Its Fourier Transform

  • Adrian Burian
  • Jukka Saarinen
  • Pauli Kuosmanen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2085)


In this paper the problem of image restoration from its Fourier spectrum magnitude is shown to be NP-complete. We propose the use of recurrent neural networks for solving the problem. The neural network incorporates the constants related to the real and imaginary parts of the image spectrum. The solution is provided by the steady state of the neural network, then is verified and eventually improved with the iterative Fourier transform algorithm. The obtained simulation results demonstrate the high efficiency of the proposed approach.


Neural Network Recurrent Neural Network Implementation Purpose Maximum Entropy Method Nonlinear Optimization Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Adrian Burian
    • 1
  • Jukka Saarinen
    • 1
  • Pauli Kuosmanen
    • 1
  1. 1.Tampere University of TechnologyTampereFinland

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