| Texto completo | |
| Autor(es): |
Pires, Rafael
;
Levada, Alexandre L. M.
;
Souza, Gustavo B.
;
Pereira, Luis A. M.
;
Santos, Daniel F. S.
;
Papa, Joao P.
;
IEEE
Número total de Autores: 7
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2017 30TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI); v. N/A, p. 7-pg., 2017-01-01. |
| Resumo | |
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. (AU) | |
| Processo FAPESP: | 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo? |
| Beneficiário: | Alexandre Xavier Falcão |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 16/19403-6 - Modelos de aprendizado baseados em energia e suas aplicações |
| Beneficiário: | João Paulo Papa |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 15/09169-3 - Adaptação de domínio com supervisão mínima em problemas multimídia |
| Beneficiário: | Luis Augusto Martins Pereira |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |