Abstract
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.
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Burian, A., Saarinen, J., Kuosmanen, P. (2001). Neural Networks for Image Restoration from the Magnitude of Its Fourier Transform. In: Mira, J., Prieto, A. (eds) Bio-Inspired Applications of Connectionism. IWANN 2001. Lecture Notes in Computer Science, vol 2085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45723-2_37
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DOI: https://doi.org/10.1007/3-540-45723-2_37
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