Efficient image restoration using Cellular Neural Networks


Celebi M., Guzelis C.

1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 97), Munich, Germany, 21 - 24 April 1997, pp.3409-3412 identifier

  • Publication Type: Conference Paper / Full Text
  • City: Munich
  • Country: Germany
  • Page Numbers: pp.3409-3412

Abstract

In this paper, a 3-D Cellular Neural Network (CNN) is applied for restoration of degraded images. It is known that regularized or Maximum a Posteriori estimation based image restoration problems can be formulated as the minimization of the Lyapunov function of the discrete-time Hopfield network. Recently, this Lyapunov function based design method has been extended to the continuous-time Hopfield network and to the continuous-time CNN operating either in a binary steady-state output mode or in a real-valued steady-state output mode. This paper considers 3-D CNN in the binary mode, which needs eight binary (nonredundant) neurons only for each image pixel thus reducing the computational overhead, and introduces a hardware annealing approach to overcome bad local minima problem due to binary mode of operation and nonredundant representation.