Deep unfolding Network for Hyperspectral Image Super-Resolution with Automatic Exposure Correction
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914714292846592 |
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| author | Fang, Yuan Liu, Yipeng Chen, Jie Long, Zhen Li, Ao Chi, Chong-Yung Zhu, Ce |
| author_facet | Fang, Yuan Liu, Yipeng Chen, Jie Long, Zhen Li, Ao Chi, Chong-Yung Zhu, Ce |
| contents | In recent years, the fusion of high spatial resolution multispectral image (HR-MSI) and low spatial resolution hyperspectral image (LR-HSI) has been recognized as an effective method for HSI super-resolution (HSI-SR). However, both HSI and MSI may be acquired under extreme conditions such as night or poorly illuminating scenarios, which may cause different exposure levels, thereby seriously downgrading the yielded HSISR. In contrast to most existing methods based on respective low-light enhancements (LLIE) of MSI and HSI followed by their fusion, a deep Unfolding HSI Super-Resolution with Automatic Exposure Correction (UHSR-AEC) is proposed, that can effectively generate a high-quality fused HSI-SR (in texture and features) even under very imbalanced exposures, thanks to the correlation between LLIE and HSI-SR taken into account. Extensive experiments are provided to demonstrate the state-of-the-art overall performance of the proposed UHSR-AEC, including comparison with some benchmark peer methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_09096 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep unfolding Network for Hyperspectral Image Super-Resolution with Automatic Exposure Correction Fang, Yuan Liu, Yipeng Chen, Jie Long, Zhen Li, Ao Chi, Chong-Yung Zhu, Ce Image and Video Processing Computer Vision and Pattern Recognition In recent years, the fusion of high spatial resolution multispectral image (HR-MSI) and low spatial resolution hyperspectral image (LR-HSI) has been recognized as an effective method for HSI super-resolution (HSI-SR). However, both HSI and MSI may be acquired under extreme conditions such as night or poorly illuminating scenarios, which may cause different exposure levels, thereby seriously downgrading the yielded HSISR. In contrast to most existing methods based on respective low-light enhancements (LLIE) of MSI and HSI followed by their fusion, a deep Unfolding HSI Super-Resolution with Automatic Exposure Correction (UHSR-AEC) is proposed, that can effectively generate a high-quality fused HSI-SR (in texture and features) even under very imbalanced exposures, thanks to the correlation between LLIE and HSI-SR taken into account. Extensive experiments are provided to demonstrate the state-of-the-art overall performance of the proposed UHSR-AEC, including comparison with some benchmark peer methods. |
| title | Deep unfolding Network for Hyperspectral Image Super-Resolution with Automatic Exposure Correction |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.09096 |