Estimation of the noise level function based on a non-parametric detection of homogeneous image regions
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| hal.structure.identifier | Laboratoire Bordelais de Recherche en Informatique [LaBRI] | |
| dc.contributor.author | SUTOUR, Camille | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | DELEDALLE, Charles-Alban | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | AUJOL, Jean-François | |
| dc.date.accessioned | 2024-04-04T03:11:06Z | |
| dc.date.available | 2024-04-04T03:11:06Z | |
| dc.date.issued | 2015-11-17 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/193743 | |
| dc.description.abstractEn | We propose a two-step algorithm that automatically estimates the noise level function of stationary noise from a single image, i.e., the noise variance as a function of the image intensity. First, the image is divided into small square regions and a non-parametric test is applied to decide weather each region is homogeneous or not. Based on Kendall's τ coefficient (a rank-based measure of correlation), this detector has a non-detection rate independent on the unknown distribution of the noise, provided that it is at least spatially uncorrelated. Moreover, we prove on a toy example, that its overall detection error vanishes with respect to the region size as soon as the signal to noise ratio level is non-zero. Once homogeneous regions are detected, the noise level function is estimated as a second order polynomial minimizing the ℓ 1 error on the statistics of these regions. Numerical experiments show the efficiency of the proposed approach in estimating the noise level function, with a relative error under 10% obtained on a large data set. We illustrate the interest of the approach for an image denoising application. | |
| dc.language.iso | en | |
| dc.publisher | Society for Industrial and Applied Mathematics | |
| dc.subject.en | non-parametric detection | |
| dc.subject.en | signal-dependent noise | |
| dc.subject.en | Noise level estimation | |
| dc.subject.en | least absolute deviation | |
| dc.title.en | Estimation of the noise level function based on a non-parametric detection of homogeneous image regions | |
| dc.type | Article de revue | |
| dc.identifier.doi | 10.1137/15M1012682 | |
| dc.subject.hal | Informatique [cs]/Traitement des images | |
| bordeaux.journal | SIAM Journal on Imaging Sciences | |
| bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-01138809 | |
| hal.version | 1 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-01138809v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=SIAM%20Journal%20on%20Imaging%20Sciences&rft.date=2015-11-17&rft.au=SUTOUR,%20Camille&DELEDALLE,%20Charles-Alban&AUJOL,%20Jean-Fran%C3%A7ois&rft.genre=article |
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