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Fast External Denoising Using Pre-Learned Transformations
| hal.structure.identifier | University of California [San Diego] [UC San Diego] | |
| dc.contributor.author | PARAMESWARAN, Shibin | |
| dc.contributor.author | ENMING, Luo | |
| hal.structure.identifier | University of California [San Diego] [UC San Diego] | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | DELEDALLE, Charles | |
| hal.structure.identifier | University of California [San Diego] [UC San Diego] | |
| dc.contributor.author | NGUYEN, Truong | |
| dc.date.accessioned | 2024-04-04T03:09:21Z | |
| dc.date.available | 2024-04-04T03:09:21Z | |
| dc.date.issued | 2017-08-01 | |
| dc.date.conference | 2017-07-22 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/193597 | |
| dc.description.abstractEn | We introduce a new external denoising algorithm that utilizes pre-learned transformations to accelerate filter calculations during runtime. The proposed fast external denoising (FED) algorithm shares characteristics of the powerful Targeted Image Denoising (TID) and Expected Patch Log-Likelihood (EPLL) algorithms. By moving computationally demanding steps to an offline learning stage, the proposed approach aims to find a balance between processing speed and obtaining high quality denoising estimates. We evaluate FED on three datasets with targeted databases (text, face and license plates) and also on a set of generic images without a targeted database. We show that, like TID, the proposed approach is extremely effective when the transformations are learned using a targeted database. We also demonstrate that FED converges to competitive solutions faster than EPLL and is orders of magnitude faster than TID while providing comparable denoising performance. | |
| dc.language.iso | en | |
| dc.source.title | IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 29-37 | |
| dc.title.en | Fast External Denoising Using Pre-Learned Transformations | |
| dc.type | Communication dans un congrès | |
| dc.subject.hal | Informatique [cs]/Traitement des images | |
| dc.subject.hal | Informatique [cs]/Traitement du signal et de l'image | |
| bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.conference.title | IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops | |
| bordeaux.country | US | |
| bordeaux.title.proceeding | IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 29-37 | |
| bordeaux.conference.city | Honolulu | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-01577541 | |
| hal.version | 1 | |
| hal.invited | non | |
| hal.proceedings | oui | |
| hal.conference.end | 2017-07-25 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-01577541v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=IEEE%20Conference%20on%20Computer%20Vision%20and%20Pattern%20Recognition%20(CVPR)%20Workshops,%202017,%20pp.%2029-37&rft.date=2017-08-01&rft.au=PARAMESWARAN,%20Shibin&ENMING,%20Luo&DELEDALLE,%20Charles&NGUYEN,%20Truong&rft.genre=unknown |
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