Non-linear aggregation of filters to improve image denoising

Benjamin Guedj (1, 2, 3, 4, 5) , Juliette Rengot (6)

1 UCL-CS - Department of Computer science [University College of London]
2 MODAL - MOdel for Data Analysis and Learning
3 UCL - University College of London [London]
4 Inria-CWI - Inria-CWI
5 Inria-London - The Inria London Programme
6 ENPC - École des Ponts ParisTech

We introduce a novel aggregation method to efficiently perform image denoising. Preliminary filters are aggregated in a non-linear fashion, using a new metric of pixel proximity based on how the pool of filters reaches a consensus. We provide a theoretical bound to support our aggregation scheme, its numerical performance is illustrated and we show that the aggregate significantly outperforms each of the preliminary filters.

Mots clés: Ensemble methods; Image denoising; Statistical aggregation; Collaborative filtering

Domaines: Machine Learning [stat.ML]; Méthodologie [stat.ME]; Apprentissage [cs.LG]; Traitement des images [eess.IV]

Benjamin Guedj, Juliette Rengot. Non-linear aggregation of filters to improve image denoising. Computing Conference 2020, Jul 2020, London, United Kingdom. ⟨hal-02086856v2⟩