Enhanced anchored neighborhood regression for image super resolution based on maximized utilization on dictionary atoms and training sample patches

2021 ◽  
Author(s):  
Qin fen Yao
Keyword(s):  
Super Resolution ◽  
Training Sample ◽  
Image Super Resolution ◽  
Anchored Neighborhood Regression ◽  
And Training

scholarly journals Remote Sensing Image Super-resolution Based on Sparse Representation

2018 ◽  
Vol 232 ◽  
pp. 02037
Author(s):  
Fuzhen Zhu ◽  
Yue Liu ◽  
Xin Huang ◽  
Haitao Zhu
Keyword(s):  
Remote Sensing ◽  
Sparse Representation ◽  
Super Resolution ◽  
Remote Sensing Image ◽  
Training Sample ◽  
Mean Value ◽  
Image Feature ◽  
Edge Information ◽  
Filtering Method ◽  
Image Super Resolution

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.

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