Efficient image restoration using cellular neural networks

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 Hopeld network. Recently, this Lyapunov function based design method has been extended to the continuous-time Hopeld 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 (nonredund-ant) neurons only for each image pixel thus reducing the computational overhead, and introduces a hardware anneal-ing approach t o o v ercome bad local minima problem due to binary mode of operation and nonredundant representation.