Optimizing the Data Adaptive Dual Domain Denoising Algorithm

Nicola Pierazzo (1) , Jean-Michel Morel (1) , Gabriele Facciolo (2, 3, 4)

1 CMLA - Centre de Mathématiques et de Leurs Applications
2 imagine [Marne-la-Vallée]
3 ENPC - École des Ponts ParisTech
4 LIGM - Laboratoire d'Informatique Gaspard-Monge

This paper presents two new strategies that greatly improve the execution time of the DA3D Algorithm, a new denoising algorithm with state-of-the-art results. First, the weight map used in DA3D is implemented as a quad-tree. This greatly reduces the time needed to search the minimum weight, greatly reducing the overall computation time. Second, a simple but effective tiling strategy is shown to work in order to allow the parallel execution of the algorithm. This allows the implementation of DA3D in a parallel architecture. Both these improvements do not affect the quality of the output.

Domaines: Traitement des images [eess.IV]; Traitement du signal et de l'image [eess.SP]; Modélisation et simulation; Vision par ordinateur et reconnaissance de formes [cs.CV]

Nicola Pierazzo, Jean-Michel Morel, Gabriele Facciolo. Optimizing the Data Adaptive Dual Domain Denoising Algorithm. CIARP, 2015, Montevideo, Uruguay. ⟨10.1007/978-3-319-25751-8_43⟩. ⟨hal-01240855⟩