@inproceedings{23dc601e16814f35aac31f4602371745,
title = "Smooth Coupled Tucker Decomposition for Hyperspectral Image Super-Resolution",
abstract = "Hyperspectral image processing methods based on Tucker decomposition by utilizing low-rank and sparse priors are sensitive to the model order, and merely utilizing the global structural information. After statistical analysis on hyperspectral images, we find that the smoothness underlying hyperspectral image encoding local structural information is ubiquity in each mode. Based on this observation, we propose a novel smooth coupled Tucker decomposition scheme with two smoothness constraints imposed on the subspace factor matrices to reveal the local structural information of hyperspectral image. In addition, efficient algorithms are designed and experimental results demonstrate the effectiveness of selecting optimal model order for hyperspectral image super-resolution due to the integration of the subspace smoothness.",
keywords = "Hyperspectral image, Smoothness, Super-resolution, Tucker decomposition",
author = "Yuanyang Bu and Yongqiang Zhao and Jize Xue and Chan, \{Jonathan Cheung Wai\}",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 4th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2021 ; Conference date: 29-10-2021 Through 01-11-2021",
year = "2021",
doi = "10.1007/978-3-030-88010-1\_20",
language = "英语",
isbn = "9783030880095",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "238--248",
editor = "Huimin Ma and Liang Wang and Changshui Zhang and Fei Wu and Tieniu Tan and Yaonan Wang and Jianhuang Lai and Yao Zhao",
booktitle = "Pattern Recognition and Computer Vision - 4th Chinese Conference, PRCV 2021, Proceedings",
}