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...
| Authors: | ; |
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| Format: | Article |
| Status: | Accepted version |
| Publication date: | 2017 |
| Country: | España |
| Institution: | Universitat Pompeu Fabra |
| Repository: | Repositorio Digital de la UPF |
| OAI Identifier: | oai:repositori.upf.edu:10230/37095 |
| Online Access: | http://hdl.handle.net/10230/37095 http://dx.doi.org/10.1109/TIP.2017.2681421 |
| Access Level: | Open access |
| Keyword: | Image denoising Patch-based method Patch similarity Affine invariance |
| Summary: | 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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