In 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.

The work of G. Ghimpeteanu, T. Batard, and/nM. Bertalmío was supported in part by the Spanish Government under/nGrant TIN2012-38112, in part by the Icrea Academia Award, and in/npart by the European Research Council under Grant 306337. The work/nof S. Levine was supported by the Na/ntional Science Foundation under/nGrant NSF-DMS 1320829.