Open Access
ARTICLE
Robust Core Tensor Dictionary Learning with Modified Gaussian Mixture Model for Multispectral Image Restoration
Leilei Geng1, Chaoran Cui1, Qiang Guo1, Sijie Niu2, Guoqing Zhang3, Peng Fu4, *
1 School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, 250014, China.
2 School of Computer Science and Engineering, University of Jinan, Jinan, 250024, China.
3 School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore.
4 School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
* Corresponding Author: Peng Fu. Email: .
Computers, Materials & Continua 2020, 65(1), 913-928. https://doi.org/10.32604/cmc.2020.09975
Received 02 February 2020; Accepted 30 April 2020; Issue published 23 July 2020
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.
Keywords
Cite This Article
L. Geng, C. Cui, Q. Guo, S. Niu, G. Zhang
et al., "Robust core tensor dictionary learning with modified gaussian mixture model for multispectral image restoration,"
Computers, Materials & Continua, vol. 65, no.1, pp. 913–928, 2020.
Citations