| Full text | |
| Author(s): |
Vicente, Alexandre Nata
;
Pedrini, Helio
;
IEEE
Total Authors: 3
|
| Document type: | Journal article |
| Source: | 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC); v. N/A, p. 6-pg., 2016-01-01. |
| Abstract | |
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) | |
| FAPESP's process: | 11/22749-8 - Challenges in exploratory visualization of multidimensional data: paradigms, scalability and applications |
| Grantee: | Luis Gustavo Nonato |
| Support Opportunities: | Research Projects - Thematic Grants |