Non Local Means Image Filtering Using Clustering
In this work we study improvements for the Non Local Means image filter using clustering in the space of patches. Patch clustering it is proposed to guide the selection of the best patches to be used to filter each pixel. Besides clustering, we incorporate spatial coherence keeping some local patches extracted from a local search window around each pixel. Therefore, for each pixel we use local patches and non local ones extracted from the corresponding cluster. The proposed method outperforms classical Non Local Means filter and also, when compared with other methods from the literature based on random sampling, our results confirm the benefits of sampling inside clusters combined with local information.
Work partially supported by ANII and Stic-Amsud. Thanks to Andrés Almansa for valuable discussions.