@inproceedings{855b35da05c0491b98e7e843e0e07953,
title = "Hyperspectral Image Joint Super-resolution via Implicit Neural Representation",
abstract = "Hyperspectral image (HSI) joint super-resolution (SR) in both spatial and spectral dimensions is an area of increasing interest in HSI processing. Although recent advances in deep learning (DL) frameworks have greatly improved the performance of joint SR reconstruction, existing methods learn discrete representations of HSI, ignoring real-world signals' continuous nature. In this paper, we propose a joint SR method based on implicit neural representation (INR), which learns local continuous representations of high spatial resolution hyperspectral images from the discrete inputs. Experiments on joint SR demonstrate that our method can achieve superior performance in comparison with state-of-the-art methods.",
keywords = "Hyperspectral image (HSI), implicit neural representation (INR), joint super-resolution",
author = "Jizhou Zhang and Tingfa Xu and Shenwang Jiang and Yuhan Zhang and Jianan Li",
note = "Publisher Copyright: {\textcopyright} 2023 SPIE.; 2022 Conference on Infrared, Millimeter, Terahertz Waves and Applications, IMT 2022 ; Conference date: 20-09-2022 Through 22-09-2022",
year = "2023",
doi = "10.1117/12.2661749",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Songlin Zhuang and Junhao Chu",
booktitle = "Conference on Infrared, Millimeter, Terahertz Waves and Applications, IMT 2022",
address = "United States",
}