Affine non-local means image denoising
This 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...
| Autores: | , |
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| Formato: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2017 |
| País: | España |
| Recursos: | Universitat Pompeu Fabra |
| Repositorio: | Repositorio Digital de la UPF |
| OAI Identifier: | oai:repositori.upf.edu:10230/37095 |
| Acesso em linha: | http://hdl.handle.net/10230/37095 http://dx.doi.org/10.1109/TIP.2017.2681421 |
| Access Level: | acceso abierto |
| Palavra-chave: | Image denoising Patch-based method Patch similarity Affine invariance |
| Resumo: | This 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. |
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