Cargando…
Super-resolution CT Image Reconstruction Based on Dictionary Learning and Sparse Representation
In this paper, a single-computed tomography (CT) image super-resolution (SR) reconstruction scheme is proposed. This SR reconstruction scheme is based on sparse representation theory and dictionary learning of low- and high-resolution image patch pairs to improve the poor quality of low-resolution C...
Autores principales: | Jiang, Changhui, Zhang, Qiyang, Fan, Rui, Hu, Zhanli |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5996061/ https://www.ncbi.nlm.nih.gov/pubmed/29892023 http://dx.doi.org/10.1038/s41598-018-27261-z |
Ejemplares similares
-
Low-Dose Computed Tomography Image Super-Resolution Reconstruction via Random Forests
por: Gu, Peijian, et al.
Publicado: (2019) -
Noise reduction of diffusion tensor images by sparse representation and dictionary learning
por: Kong, Youyong, et al.
Publicado: (2016) -
Super-resolution of X-ray CT images of rock samples by sparse representation: applications to the complex texture of serpentinite
por: Omori, Toshiaki, et al.
Publicado: (2023) -
Orthogonal Procrustes Analysis for Dictionary Learning in Sparse Linear Representation
por: Grossi, Giuliano, et al.
Publicado: (2017) -
An improved statistical iterative algorithm for sparse-view and limited-angle CT image reconstruction
por: Hu, Zhanli, et al.
Publicado: (2017)