A new framework for remote sensing image super-resolution: Sparse representation-based method by processing dictionaries with multi-type features

2016 ◽  
Vol 64 ◽  
pp. 63-75 ◽  
Author(s):  
Wei Wu ◽  
Xiaomin Yang ◽  
Kai Liu ◽  
Yiguang Liu ◽  
Binyu Yan ◽  
...  
2018 ◽  
Vol 232 ◽  
pp. 02037
Author(s):  
Fuzhen Zhu ◽  
Yue Liu ◽  
Xin Huang ◽  
Haitao Zhu

In order to obtain higher resolution remote sensing images with more details, an improved sparse representation remote sensing image super-resolution reconstruction(SRR) algorithm is proposed. First, remote sensing image is preprocessed to obtain the required training sample image; then, the KSVD algorithm is used for dictionary training to obtain the high-low resolution dictionary pairs; finally, the image feature extraction block is represented, which is improved by using adaptive filtering method. At the same time, the mean value filtering method is used to improve the super-resolution reconstruction iterative calculation. Experiment results show that, compared with the most advanced sparse representation super-resolution algorithm, the improved sparse representation super-resolution method can effectively avoid the loss of edge information of SRR image and obtain a better super-resolution reconstruction effect. The texture details are more abundant in subjective vision, the PSNR is increased about 1 dB, and the structure similarity (SSIM) is increased about 0.01.


2019 ◽  
Vol 27 (3) ◽  
pp. 718-725
Author(s):  
朱福珍 ZHU Fu-zhen ◽  
刘 越 LIU Yue ◽  
黄 鑫 HUANG Xin ◽  
白鸿一 BAI Hong-yi ◽  
巫 红 WU Hong

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