Hyperspectral image joint super-resolution via implicit neural representation

Jizhou Zhang; Tingfa Xu; Shengwang Jiang; Yuhan Zhang; Jianan Li

doi:10.1117/12.2661749

12 April 2023 Hyperspectral image joint super-resolution via implicit neural representation

Jizhou Zhang, Tingfa Xu, Shengwang Jiang, Yuhan Zhang, Jianan Li

Proceedings Volume 12565, Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022); 125650Z (2023) https://doi.org/10.1117/12.2661749
Event: Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022), 2022, Shanghai, China

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.

Citation Download Citation

Jizhou Zhang, Tingfa Xu, Shengwang Jiang, Yuhan Zhang, and Jianan Li "Hyperspectral image joint super-resolution via implicit neural representation", Proc. SPIE 12565, Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022), 125650Z (12 April 2023); https://doi.org/10.1117/12.2661749

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