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[bibtex]@InProceedings{Li_2022_WACV, author = {Li, Ke and Dai, Dengxin and Van Gool, Luc}, title = {Hyperspectral Image Super-Resolution With RGB Image Super-Resolution as an Auxiliary Task}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2022}, pages = {3193-3202} }
Hyperspectral Image Super-Resolution With RGB Image Super-Resolution as an Auxiliary Task
Abstract
This work studies Hyperspectral image (HSI) super-resolution (SR). HSI SR is characterized by high-dimensional data and a limited amount of training exam-ples. This raises challenges for training deep neural net-works that are known to be data hungry. This work ad-dresses this issue with two contributions. First, we observethat HSI SR and RGB image SR are correlated and developa novel multi-tasking network to train them jointly so thatthe auxiliary task RGB image SR can provide additionalsupervision and regulate the network training. Second,we extend the network to a semi-supervised setting so thatit can learn from datasets containing only low-resolutionHSIs. With these contributions, our method is able to learnhyperspectral image super-resolution from heterogeneousdatasets and lifts the requirement for having a large amountof HD HSI training samples. Extensive experiments onthree standard datasets show that our method outperformsexisting methods significantly and underpin the relevance ofour contributions.
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