Fedorov, VadimBallester, Coloma2019-04-112019-04-112017Fedorov V, Ballester C. Affine non-local means image denoising. IEEE Trans Image Process. 2017;26(5):2137-48. DOI: 10.1109/TIP.2017.26814211057-7149http://hdl.handle.net/10230/37095This work presents an extension of the Non-Local Means denoising method, that effectively exploits the affine invariant self-similarities present in images of real scenes. Our method provides a better image denoising result by grounding on the fact that in many occasions similar patches exist in the image but have undergone a transformation. The proposal uses an affine invariant patch similarity measure that performs an appropriate patch comparison by automatically and intrinsically adapting the size and shape of the patches. As a result, more similar patches are found and appropriately used. We show that this image denoising method achieves top-tier performance in terms of PSNR, outperforming consistently the results of the regular Non-Local Means, and that it provides state-of-the-art qualitative results.application/pdfeng© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/TIP.2017.2681421info:eu-repo/semantics/articlehttp://dx.doi.org/10.1109/TIP.2017.2681421Image denoisingPatch-based methodPatch similarityAffine invarianceinfo:eu-repo/semantics/openAccess