Robust Noisy Image Super-Resolution Using $$\ell _1$$ -norm Regularization and Non-local Constraint

2017 ◽  
pp. 34-49
Author(s):  
Bo Yue ◽  
Shuang Wang ◽  
Xuefeng Liang ◽  
Licheng Jiao
Keyword(s):  
Super Resolution ◽  
Noisy Image ◽  
Local Constraint ◽  
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.

Single image super-resolution using regularization of non-local steering kernel regression

2016 ◽  
Vol 123 ◽  
pp. 53-63 ◽  
Author(s):  
Kaibing Zhang ◽  
Xinbo Gao ◽  
Jie Li ◽  
Hongxing Xia
Keyword(s):  
Kernel Regression ◽  
Super Resolution ◽  
Single Image ◽  
Non Local ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Non-local feature back-projection for image super-resolution

2016 ◽  
Vol 10 (5) ◽  
pp. 398-408 ◽  
Author(s):  
Xin Zhang ◽  
Yuanfeng Zhou ◽  
Qian Liu ◽  
Caiming Zhang ◽  
Xuemei Li
Keyword(s):  
Super Resolution ◽  
Local Feature ◽  
Back Projection ◽  
Non Local ◽  
Image Super Resolution

Image super-resolution using non-local Gaussian process regression

2016 ◽  
Vol 194 ◽  
pp. 95-106 ◽  
Author(s):  
Haijun Wang ◽  
Xinbo Gao ◽  
Kaibing Zhang ◽  
Jie Li
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Super Resolution ◽  
Non Local ◽  
Image Super Resolution

scholarly journals Joint Prior Learning for Visual Sensor Network Noisy Image Super-Resolution

Sensors ◽  
2016 ◽  
Vol 16 (3) ◽  
pp. 288 ◽  
Author(s):  
Bo Yue ◽  
Shuang Wang ◽  
Xuefeng Liang ◽  
Licheng Jiao ◽  
Caijin Xu
Keyword(s):  
Sensor Network ◽  
Super Resolution ◽  
Noisy Image ◽  
Prior Learning ◽  
Visual Sensor ◽  
Visual Sensor Network ◽  
Image Super Resolution
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