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Blancheur du résidu pour le débruitage d'image

Abstract : We propose an advanced use of the whiteness hypothesis on the noise to imrove denoising performances. We show the interest of evaluating the residual whiteness by correlation measures in multiple applications. First, in a variational denoising framework, we show that a cost function locally constraining the residual whiteness can replace the L2 norm commonly used in the white Gaussian case, while significantly improving the denoising performances. This term is then completed by cost function constraining the residual raw moments which are a mean to control the residual distribution. In the second part of our work, we propose an alternative to the likelihood ratio, leading to the L2 norm in the white Gaussian case, to evaluate the dissimilarity between noisy patches. The introduced metric, based on the autocorrelation of the patches difference, achieves better performances both for denoising and similar patches recognition. Finally, we tackle the no reference quality evaluation and the local model choice problems. Once again, the residual whiteness bring a meaningful information to locally estimate the truthfulness of the denoising.
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Submitted on : Tuesday, July 3, 2018 - 3:11:14 PM
Last modification on : Friday, July 31, 2020 - 10:44:09 AM
Long-term archiving on: : Monday, October 1, 2018 - 10:58:48 AM


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  • HAL Id : tel-01828883, version 1


Paul Riot. Blancheur du résidu pour le débruitage d'image. Traitement des images [eess.IV]. Télécom ParisTech, 2018. Français. ⟨NNT : 2018ENST0006⟩. ⟨tel-01828883⟩



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