Ghimpeteanu, GabrielaBatard, ThomasBertalmío, MarceloLevine, Stacey2016-06-172016-06-172016Ghimpeteanu G, Batard T, Bertalmío M, Levine S. A decomposition framework for image denoising algorithms. IEEE Transactions on Image Processing. 2016;25(1):388-99. DOI: 10.1109/TIP.2015.24984131057-7149http://hdl.handle.net/10230/26940In this paper, we consider an image decomposition model that provides a novel framework for image denoising. The model computes the components of the image to be processed in a moving frame that encodes its local geometry (directions of gradients and level lines). Then, the strategy we develop is to denoise the components of the image in the moving frame in order to preserve its local geometry, which would have been more affected if processing the image directly. Experiments on a whole image database tested with several denoising methods show that this framework can provide better results than denoising the image directly, both in terms of Peak signal-to-noise ratio and Structural similarity index metrics.application/pdfeng© 2015 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1109/TIP.2015.2498413Image denoisingLocal variational methodPatch-based methodDifferential geometryinfo:eu-repo/semantics/openAccess