Fast External Denoising Using Pre-Learned Transformations

Abstract : 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.
Type de document :
Communication dans un congrès
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Jul 2017, Honolulu, United States. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 29-37 2017
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https://hal.archives-ouvertes.fr/hal-01577541
Contributeur : Charles-Alban Deledalle <>
Soumis le : samedi 26 août 2017 - 04:17:02
Dernière modification le : dimanche 27 août 2017 - 01:04:19

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  • HAL Id : hal-01577541, version 1

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Shibin Parameswaran, Luo Enming, Charles Deledalle, Truong Nguyen. Fast External Denoising Using Pre-Learned Transformations. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Jul 2017, Honolulu, United States. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 29-37 2017. <hal-01577541>

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