Image super-resolution based on shock filter and non-local means

2014 ◽  
Author(s):  
Taichi Yoshida ◽  
Masaaki Ikehara
Keyword(s):  
Super Resolution ◽  
Local Means ◽  
Non Local ◽  
Shock Filter ◽  
Image Super Resolution

Depth image super-resolution algorithm based on structural features and non-local means

2018 ◽  
Vol 14 (5) ◽  
pp. 391-395
Author(s):  
Wang Jing ◽  
Wei-Zhong Zhang ◽  
Bao-Xiang Huang ◽  
Huan Yang
Keyword(s):  
Super Resolution ◽  
Structural Features ◽  
Depth Image ◽  
Local Means ◽  
Non Local ◽  
Image Super Resolution ◽  
Resolution Algorithm

scholarly journals Single image super resolution based on multi-scale structure and non-local smoothing

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wenyi Wang ◽  
Jun Hu ◽  
Xiaohong Liu ◽  
Jiying Zhao ◽  
Jianwen Chen
Keyword(s):  
Super Resolution ◽  
Principal Component ◽  
Feature Mapping ◽  
Multi Scale ◽  
Non Local ◽  
Image Super Resolution ◽  
Gaussian Blur ◽  
Single Image Super Resolution ◽  
Global And Local ◽  
Bicubic Interpolation

AbstractIn this paper, we propose a hybrid super-resolution method by combining global and local dictionary training in the sparse domain. In order to present and differentiate the feature mapping in different scales, a global dictionary set is trained in multiple structure scales, and a non-linear function is used to choose the appropriate dictionary to initially reconstruct the HR image. In addition, we introduce the Gaussian blur to the LR images to eliminate a widely used but inappropriate assumption that the low resolution (LR) images are generated by bicubic interpolation from high-resolution (HR) images. In order to deal with Gaussian blur, a local dictionary is generated and iteratively updated by K-means principal component analysis (K-PCA) and gradient decent (GD) to model the blur effect during the down-sampling. Compared with the state-of-the-art SR algorithms, the experimental results reveal that the proposed method can produce sharper boundaries and suppress undesired artifacts with the present of Gaussian blur. It implies that our method could be more effect in real applications and that the HR-LR mapping relation is more complicated than bicubic interpolation.

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