| Texto completo | |
| Autor(es): |
Vicente, Alexandre Nata
;
Pedrini, Helio
;
IEEE
Número total de Autores: 3
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC); v. N/A, p. 6-pg., 2016-01-01. |
| Resumo | |
Spatial resolution enhancement of license plate images in real scenarios plays an important role in the fields of criminal investigation and forensic science. This paper presents a learning-based single-image super-resolution method that uses a priori knowledge of the input as the plate images captured at poor quality and very low resolution. The proposed method employs a decision tree to classify the input image and the classification results are used to weight the image patches in the reconstruction step. Additionally, a histogram equalization is performed to improve the performance of the classifier. Experiments conducted on synthetic and real-world images demonstrate that the proposed method is capable of producing satisfactory results. (AU) | |
| Processo FAPESP: | 11/22749-8 - Desafios em visualização exploratória de dados multidimensionais: novos paradigmas, escalabilidade e aplicações |
| Beneficiário: | Luis Gustavo Nonato |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |