Two-dimensional random adaptive sampling for image scanning

An efficient sampling algorithm for image scanning is proposed, suitable to represent "interesting" objects, defined as a set of spatially close measured values that springs out from a background noise (as in applied geophysics in the process of anomaly detection). This method generates a map of pixels randomly distributed in the plane and able to cover all the image with a reduced number of points with respect to a regular scanning. Simulation results show that a saving factor of about 50% is obtained without information loss. This result can be proved also by using a simplified model of the sampling mechanism. The algorithm is able to detect the presence of an object emerging from a low energy background and to adapt the sampling interval to the shape of the detected object. In this way, all of the interesting objects are well represented and can be adequately reconstructed, while the roughly sampling in the background produces an imperfect reconstruction. Simulation results show that the method is feasible with good performances and moderate complexity.