Image restoration preserving discontinuities : the bayesan approach and neural networks

Recently, methods which permit discontinuities to be taken into account have been investigated with respect to solving visual reconstruction problems. These methods, both deterministic and probabilistic, present formidable computational costs, due to the complexity of the algorithms used and the dimension of the problems treated. To reduce execution times, new computational implementations based on parallel architectures such as neural networks have been proposed. In this paper the edge preserving restoration of piecewise smooth images is formulated in terms of a probabilistic approach, and a MAP estimate algorithm is proposed which could be implemented on a hybrid neural network. We adopt a model for the image consisting of two coupled MRFs', one representing the intensity and the other the discontinuities, in such a way as to introduce prior probabilistic knowledge about global and local features. According to an annealing schedule, the solution is obtained iteratively by means of a sequence in which deterministic steps alternate with probabilistic ones. The algorithm is suitable for implementation on a hybrid architecture made up of a grid of digital processors interacting with a linear neural network which supports most of the computational costs.