LAUSR

In this work we discuss methods for image ringing detection and suppression that are based on the sparse representations approach and suggest a new ringing suppression method. The ringing detection algorithm is based on construction of the synthetic dictionary that is used to represent ringing effect as a sum of blurred edge and pure ringing component. This decomposition enables us to estimate image ringing level. We analyze two ringing suppression methods. First method is based on learning joint dictionaries and shows good performance for the whole image on average. However for high ringing levels the performance of this method decreases due to the influence of the ringing artefact on the sparse representation parameters. The second method is based on separate learning of natural images dictionary and pure ringing dictionary and it does not suffer from this problem. In this article we present a new ringing suppression method that is based on the method using separate dictionaries. The method works best in the areas of edges and for higher levels of ringing effect.