Super resolution method adapted to spatial contrast

In this paper we present an effective method for super resolution (SR). The proposed method is inspired by the Non-Local Means (NLM) algorithm but shows a lot of improvements over it. Firstly, we show the sensitivity to the standard deviation parameter of the NLM algorithm in detail regions that has low contrast. This analysis leads to an improvement of the NLM algorithm by selecting the best neighbors for each pixel in the SR image to be reconstructed. Secondly, this novel selection of neighbors is combined with a segmentation step in order to build a SR framework that can be spatially adapted to contrast. The proposed method allows reconstruction of SR images that conserves low-contrast detail while ensuring noise canceling.