Blind Image Super Resolution Using Deep Unsupervised Learning
The goal of single image super resolution (SISR) is to recover a high-resolution (HR) image from a low-resolution (LR) image. Deep learning based methods have recently made a remarkable performance gain in terms of both the effectiveness and efficiency for SISR. Most existing methods have to be trai...
Guardado en:
Autores principales: | Kazuhiro Yamawaki, Yongqing Sun, Xian-Hua Han |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/e69d84375f974e61a94f0ec7054d7e12 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Pixel-Level Kernel Estimation for Blind Super-Resolution
por: Jaihyun Lew, et al.
Publicado: (2021) -
Automatic Unsupervised Fabric Defect Detection Based on Self-Feature Comparison
por: Zhengrui Peng, et al.
Publicado: (2021) -
A New Full-Reference Image Quality Metric for Motion Blur Profile Characterization
por: Mohammad Abdullah-Al-Mamun, et al.
Publicado: (2021) -
Smart Glass System Using Deep Learning for the Blind and Visually Impaired
por: Mukhriddin Mukhiddinov, et al.
Publicado: (2021) -
Analysis of the Possibilities of Tire-Defect Inspection Based on Unsupervised Learning and Deep Learning
por: Ivan Kuric, et al.
Publicado: (2021)