A Partitioned Modified Hopfield Neural Network Algorithm for Real-Time Image Restoration

Abstract In this paper, two image partitioning schemes are examined. The first scheme examined avoids boundary conflicts by the use of four restoration phases. The second scheme examined requires a degree of synchronization of the processors restoring adjacent regions. Both schemes avoid conflicting boundary conditions by taking into account the local image formation properties. Without any loss of processing speed, or increase in the number of processors required to restore the image, synchronizing conditions are not required in the four-phase scheme to restore the image accurately, however can be used to maximize restoration efficiency. An improved modified Hopfield neural network-based algorithm is developed to be especially applicable to the problems of real-time image processing based on the described partitioning schemes. The proposed algorithm extends the concepts involved with previous algorithms to enable faster image processing and a greater scope for using the inherent parallelism of the neural network approach to image processing. The simulation in this investigation shows that the new algorithm is able to maximize the efficiency of the described partitioning methods. This paper also presents an example of an application of the proposed algorithm to restore images degraded by motion blur.