Super-resolution for Medical Image via Sparse Representation and Adaptive M-estimator.
West Indian Med J
; 65(2): 271-276, 2015 May 11.
Article
em En
| MEDLINE
| ID: mdl-28358437
OBJECTIVE: The goal of super-resolution is to generate high-resolution images from low-resolution input images. METHODS: In this paper, a combined method based on sparse signal representation and adaptive M-estimator is proposed for single-image super-resolution. With the sparse signal representation, the correlation between the sparse representation of high-resolution patches and that of low-resolution patches for the identical image is learned as a set of joint dictionaries and a set of high-resolution patches is obtained for high- and low-resolution patches. Then the dictionaries and high-resolution patches are used to produce the high-resolution image for a low-resolution single image. RESULTS: At the post-processing phase, the adaptive M-estimator, combining the advantages of traditional L1 and L2 norms, is used to give further processing for the resultant high-resolution image, to reduce the artefact by learning and reconstitution, and improve the performance. CONCLUSION: Three experimental results show the performance improvement of the proposed algorithm over other methods.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
West Indian Med J
Ano de publicação:
2015
Tipo de documento:
Article
País de afiliação:
China
País de publicação:
Jamaica