Hyperspectral Image Super-Resolution with RGB Image Super-Resolution as an Auxiliary Task
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Date
2022
Publication Type
Conference Paper
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yes
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Abstract
This work studies Hyperspectral image (HSI) super-resolution (SR). HSI SR is characterized by high-dimensional data and a limited amount of training examples. This raises challenges for training deep neural networks that are known to be data hungry. This work addresses this issue with two contributions. First, we observe that HSI SR and RGB image SR are correlated and develop a novel multi-tasking network to train them jointly so that the auxiliary task RGB image SR can provide additional supervision and regulate the network training. Second, we extend the network to a semi-supervised setting so that it can learn from datasets containing only low-resolution HSIs. With these contributions, our method is able to learn hyperspectral image super-resolution from heterogeneous datasets and lifts the requirement for having a large amount of high resolution (HR) HSI training samples. Extensive experiments on three standard datasets show that our method outperforms existing methods significantly and underpin the relevance of our contributions. Our code can be found at https://github.com/kli8996/HSISR.git.
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published
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Book title
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Journal / series
Volume
Pages / Article No.
4039 - 4048
Publisher
IEEE
Event
22nd IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2022)
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Methods
Software
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Date created
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Organisational unit
03514 - Van Gool, Luc (emeritus) / Van Gool, Luc (emeritus)
Notes
Conference lecture held on January 7, 2022