Toward Stable, Interpretable, and Lightweight Hyperspectral Super-Resolution
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, 2023-June, pp. 22272-22281
- Issue Date:
- 2023-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1723279.pdf | Published version | 1.36 MB |
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For real applications existing HSI SR methods are not only limited to unstable performance under unknown scenarios but also suffer from high computation consumption In this paper we develop a new coordination optimization framework for stable interpretable and lightweight HSI SR Specifically we create a positive cycle between fusion and degradation estimation under a new probabilistic framework The estimated degradation is applied to fusion as guidance for a degradation aware HSI SR Under the framework we establish an explicit degradation estimation method to tackle the indeterminacy and unstable performance caused by the black box simulation in previous methods Considering the interpretability in fusion we integrate spectral mixing prior into the fusion process which can be easily realized by a tiny autoencoder leading to a dramatic release of the computation burden Based on the spectral mixing prior we then develop a partial fine tune strategy to reduce the computation cost further Comprehensive experiments demonstrate the superiority of our method against the state of the arts under synthetic and real datasets For instance we achieve a 2 3 dB promotion on PSNR with 120 times model size reduction and 4300 times FLOPs reduction under the CAVE dataset Code is available in https github com WenjinGuo DAEM
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