Multi-sensor image super-resolution with fuzzy cluster by using multi-scale and multi-view sparse coding for infrared image

2017 ◽  
Vol 76 (23) ◽  
pp. 24871-24902 ◽  
Author(s):  
Xiaomin Yang ◽  
Wei Wu ◽  
Kai Liu ◽  
Weilong Chen ◽  
Ping Zhang ◽  
...  
Keyword(s):  
Sparse Coding ◽  
Infrared Image ◽  
Super Resolution ◽  
Fuzzy Cluster ◽  
Multi Scale ◽  
Image Super Resolution

The Fast Multi-scale Convolutional Sparse Coding Based Super-Resolution for Infrared Image

2018 ◽  
Vol 30 (10) ◽  
pp. 1935
Author(s):  
Wenwen Zhang ◽  
Yusheng Han ◽  
Qinchao Huang ◽  
Guoming Xu
Keyword(s):  
Sparse Coding ◽  
Infrared Image ◽  
Super Resolution ◽  
Multi Scale

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.

Multi-scale channel network based on filer pruning for image super-resolution

Optik ◽  
2021 ◽  
Vol 236 ◽  
pp. 166641
Author(s):  
Deyun Wei ◽  
Zhaowu Wang
Keyword(s):  
Super Resolution ◽  
Channel Network ◽  
Multi Scale ◽  
Image Super Resolution

Multiple dictionary pairs learning and sparse representation-based infrared image super-resolution with improved fuzzy clustering

2017 ◽  
Vol 22 (5) ◽  
pp. 1385-1398 ◽  
Author(s):  
Xiaomin Yang ◽  
Wei Wu ◽  
Kai Liu ◽  
Weilong Chen ◽  
Zhili Zhou
Keyword(s):  
Sparse Representation ◽  
Fuzzy Clustering ◽  
Infrared Image ◽  
Super Resolution ◽  
Image Super Resolution
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