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

Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression

2012 ◽  
Vol 21 (11) ◽  
pp. 4544-4556 ◽  
Author(s):  
Kaibing Zhang ◽  
Xinbo Gao ◽  
Dacheng Tao ◽  
Xuelong Li
Keyword(s):  
Kernel Regression ◽  
Super Resolution ◽  
Single Image ◽  
Local Means ◽  
Non Local ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Single infrared image super-resolution combining non-local means with kernel regression

2013 ◽  
Vol 61 ◽  
pp. 50-59 ◽  
Author(s):  
Hui Yu ◽  
Fu-sheng Chen ◽  
Zhi-jie Zhang ◽  
Chen-sheng Wang
Keyword(s):  
Kernel Regression ◽  
Infrared Image ◽  
Super Resolution ◽  
Local Means ◽  
Non Local ◽  
Image Super Resolution

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.

scholarly journals A Novel Fast 3d Single Image Super-Resolution Algorithm

2021 ◽  
Author(s):  
Nwigbo Kenule Tuador ◽  
Duong Hung Pham ◽  
Jerome Michetti ◽  
Adrian Basarab ◽  
Denis Kouame
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Image Super Resolution ◽  
Resolution Algorithm ◽  
Single Image Super Resolution
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