Name
Abstract | ||
|---|---|---|
During the image acquisition process, some level of noise is usually added to the real data mainly due to physical limitations of the acquisition sensor, and also regarding imprecisions during the data transmission and manipulation. Therefore, the resultant image needs to be processed in order to attenuate its noise without loosing details. Machine learning approaches have been successfully used for image denoising. Among such approaches, Restricted Boltzmann Machine (RBM) is one of the most used technique for this purpose. Here, we propose to enhance the RBM performance on image denoising by adding a posterior supervision before its final denoising step. To this purpose, we propose a simple but effective approach that performs a fine-tuning in the RBM model. Experiments on public datasets corrupted by different levels of Gaussian noise support the effectiveness of the proposed approach with respect to some state-of-the-art image denoising approaches. |
Year | Venue | Field |
|---|---|---|
2017 | SIBGRAPI | Noise reduction,Computer vision,Restricted Boltzmann machine,Pattern recognition,Noise measurement,Data transmission,Non-local means,Computer science,Binary image,Artificial intelligence,Gaussian noise,Video denoising |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
9 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rafael Goncalves Pires | 1 | 2 | 3.11 |
| Daniel Felipe Silva Santos | 2 | 2 | 0.74 |
| Luís A. M. Pereira | 3 | 129 | 8.87 |
| Gustavo Botelho de Souza | 4 | 12 | 2.93 |
| Alexandre L. M. Levada | 5 | 55 | 14.76 |
| João Paulo Papa | 6 | 278 | 44.60 |