@Article{cmc.2020.09975,
AUTHOR = {Leilei Geng, Chaoran Cui, Qiang Guo, Sijie Niu, Guoqing Zhang, Peng Fu},
TITLE = {Robust Core Tensor Dictionary Learning with Modified Gaussian  Mixture Model for Multispectral Image Restoration},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {913--928},
URL = {http://www.techscience.com/cmc/v65n1/39603},
ISSN = {1546-2226},
ABSTRACT = {The multispectral remote sensing image (MS-RSI) is degraded existing multispectral camera due to various hardware limitations. In this paper, we propose a novel core 
tensor dictionary learning approach with the robust modified Gaussian mixture model for 
MS-RSI restoration. First, the multispectral patch is modeled by three-order tensor and 
high-order singular value decomposition is applied to the tensor. Then the task of MS-RSI 
restoration is formulated as a minimum sparse core tensor estimation problem. To improve 
the accuracy of core tensor coding, the core tensor estimation based on the robust modified 
Gaussian mixture model is introduced into the proposed model by exploiting the sparse 
distribution prior in image. When applied to MS-RSI restoration, our experimental results 
have shown that the proposed algorithm can better reconstruct the sharpness of the image 
textures and can outperform several existing state-of-the-art multispectral image restoration 
methods in both subjective image quality and visual perception.},
DOI = {10.32604/cmc.2020.09975}
}