Hyperspectral Image Joint Super-Resolution via Local Implicit Spatial-Spectral Function Learning
Hyperspectral image (HSI) super-resolution (SR) in both spatial and spectral dimensions is one of the most attractive research topics in HSI processing. Although recent advances in deep learning (DL) frameworks have greatly improved the performance of spatial-spectral SR reconstruction, existing met...
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IEEE
2024-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/10521696/ |
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| author | Yanan Zhang Jizhou Zhang Sijia Han |
| author_facet | Yanan Zhang Jizhou Zhang Sijia Han |
| author_sort | Yanan Zhang |
| collection | DOAJ |
| description | Hyperspectral image (HSI) super-resolution (SR) in both spatial and spectral dimensions is one of the most attractive research topics in HSI processing. Although recent advances in deep learning (DL) frameworks have greatly improved the performance of spatial-spectral SR reconstruction, existing methods learn discrete representations of HSI, ignoring real-world signals' continuous nature. Recently, Implicit Neural Representation (INR) has been applied to 3D surface reconstruction and image SR for continuous representation and has attracted increasing attention. In this paper, we propose the Local Implicit Spatial-spectral Function (LISSF), which learns a local continuous representation of high spatial resolution hyperspectral images (HR-HSI) from the discrete inputs. The model consists of a deep feature encoder and a spatial-spectral intensity decoder. The encoder converts the low spatial resolution multispectral image (LR-MSI) into deep features and the decoder predicts the intensity values at the given coordinates as output. Since the spatial-spectral coordinates are continuous, LISSF can achieve spatial-spectral SR in arbitrary scales, even extrapolating to higher resolutions not covered by the training data. Extensive experiments on spatial-spectral SR, spatial SR, and spectral SR demonstrate that LISSF can achieve superior performance in comparison with state-of-the-art methods. Moreover, ablation studies are performed on the effects of individual components of LISSF. |
| format | Article |
| id | doaj-art-fe57701be8cf4fc998ebd76ec4705c8e |
| institution | DOAJ |
| issn | 1943-0655 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-fe57701be8cf4fc998ebd76ec4705c8e2025-08-20T03:15:51ZengIEEEIEEE Photonics Journal1943-06552024-01-0116311210.1109/JPHOT.2024.339723210521696Hyperspectral Image Joint Super-Resolution via Local Implicit Spatial-Spectral Function LearningYanan Zhang0https://orcid.org/0009-0004-8129-6516Jizhou Zhang1Sijia Han2School of Arts and Media, Hubei Business College, Wuhan, ChinaMech-Mind Robotics Technologies Ltd., Beijing, ChinaLaboratory of Microwave Sensing, National Space Science Center, Chinese Academy of Science, Beijing, ChinaHyperspectral image (HSI) super-resolution (SR) in both spatial and spectral dimensions is one of the most attractive research topics in HSI processing. Although recent advances in deep learning (DL) frameworks have greatly improved the performance of spatial-spectral SR reconstruction, existing methods learn discrete representations of HSI, ignoring real-world signals' continuous nature. Recently, Implicit Neural Representation (INR) has been applied to 3D surface reconstruction and image SR for continuous representation and has attracted increasing attention. In this paper, we propose the Local Implicit Spatial-spectral Function (LISSF), which learns a local continuous representation of high spatial resolution hyperspectral images (HR-HSI) from the discrete inputs. The model consists of a deep feature encoder and a spatial-spectral intensity decoder. The encoder converts the low spatial resolution multispectral image (LR-MSI) into deep features and the decoder predicts the intensity values at the given coordinates as output. Since the spatial-spectral coordinates are continuous, LISSF can achieve spatial-spectral SR in arbitrary scales, even extrapolating to higher resolutions not covered by the training data. Extensive experiments on spatial-spectral SR, spatial SR, and spectral SR demonstrate that LISSF can achieve superior performance in comparison with state-of-the-art methods. Moreover, ablation studies are performed on the effects of individual components of LISSF.https://ieeexplore.ieee.org/document/10521696/Hyperspectral image (HSI)spatial-spectral super-resolutionimplicit neural representations (INR)local implicit spatial-spectral function (LISSF) |
| spellingShingle | Yanan Zhang Jizhou Zhang Sijia Han Hyperspectral Image Joint Super-Resolution via Local Implicit Spatial-Spectral Function Learning IEEE Photonics Journal Hyperspectral image (HSI) spatial-spectral super-resolution implicit neural representations (INR) local implicit spatial-spectral function (LISSF) |
| title | Hyperspectral Image Joint Super-Resolution via Local Implicit Spatial-Spectral Function Learning |
| title_full | Hyperspectral Image Joint Super-Resolution via Local Implicit Spatial-Spectral Function Learning |
| title_fullStr | Hyperspectral Image Joint Super-Resolution via Local Implicit Spatial-Spectral Function Learning |
| title_full_unstemmed | Hyperspectral Image Joint Super-Resolution via Local Implicit Spatial-Spectral Function Learning |
| title_short | Hyperspectral Image Joint Super-Resolution via Local Implicit Spatial-Spectral Function Learning |
| title_sort | hyperspectral image joint super resolution via local implicit spatial spectral function learning |
| topic | Hyperspectral image (HSI) spatial-spectral super-resolution implicit neural representations (INR) local implicit spatial-spectral function (LISSF) |
| url | https://ieeexplore.ieee.org/document/10521696/ |
| work_keys_str_mv | AT yananzhang hyperspectralimagejointsuperresolutionvialocalimplicitspatialspectralfunctionlearning AT jizhouzhang hyperspectralimagejointsuperresolutionvialocalimplicitspatialspectralfunctionlearning AT sijiahan hyperspectralimagejointsuperresolutionvialocalimplicitspatialspectralfunctionlearning |