Image recovery and segmentation using competitive learning in a layered network

In this study, we have used the principle of competitive learning to develop an iterative algorithm for image recovery and segmentation. Within the framework of Markov random fields (MRF's), the image recovery problem is transformed to the problem of minimization of an energy function. A local update rule for each pixel point is then developed in a stepwise fashion and is shown to be a gradient descent rule for an associated global energy function. The relationship of the update rule to Kohonen's update rule is shown. Quantitative measures of edge preservation and edge enhancement for synthetic images are introduced. As compared to recently published results using mean field approximation, our algorithm shows consistently better performance in edge preservation and comparable performance in enhancing within the boundaries. These results are based on simulation experiments on a set of synthetic images corrupted by Gaussian noise and on a set of real images.