Enhancing local-Transmitting less-Improving global
Super-resolving a natural image is an ill-posed problem. The classical approach is based on the registration and subsequent interpolation of a given set of low-resolution images. However, achieving satisfactory results typically requires the combination of a large number of them. Such an approach would be impractical over heterogeneous rate-constrained wireless networks due to the associated communication cost and limited data available. In this paper, we present an approach for local image enhancement following the finite rate of innovation sampling framework, and motivate its application to the super-resolution problem over heterogeneous networks. Local estimates can be exchanged among the nodes of the network in order to regularize the super-resolution problem while, at the same time, reduce data exchange.