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Communication Dans Un Congrès Année : 2017

Fast External Denoising Using Pre-Learned Transformations

Résumé

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.
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Dates et versions

hal-01577541 , version 1 (26-08-2017)

Identifiants

  • HAL Id : hal-01577541 , version 1

Citer

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. ⟨hal-01577541⟩

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