Robust core tensor dictionary learning with modified Gaussian mixture model for multispectral image restoration

• Main Authors: Geng, Leilei, Cui, Chaoran, Guo, Qiang, Niu, Sijie, Zhang, Guoqing, Fu, Peng
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Computer science and engineering; Multispectral Remote Sensing; Image Restoration
• Online Access: https://hdl.handle.net/10356/146882
• Institution: Nanyang Technological University

The multispectral remote sensing image (MS-RSI) is degraded existing multi-spectral 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.