# UnmixingSR: Material-aware Network with Unsupervised Unmixing as Auxiliary Task for Hyperspectral Image Super-resolution

arXiv: https://arxiv.org/abs/2407.06525 · HTML: https://mlsanity.com/paper/2407.06525/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 9 Jul 2024.

## Builds on (most cited first, 11 of 11)

- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [Deep Learning for Image Super-resolution: A Survey](https://mlsanity.com/paper/1902.06068/) (2019)
- [FcaNet: Frequency Channel Attention Networks](https://mlsanity.com/paper/2012.11879/) (2020)
- [MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning](https://mlsanity.com/paper/2001.06902/) (2020)
- [Blind Image Super-Resolution: A Survey and Beyond](https://mlsanity.com/paper/2107.03055/) (2021)
- [Self-Supervised Generalisation with Meta Auxiliary Learning](https://mlsanity.com/paper/1901.08933/) (2019)
- [Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery](https://mlsanity.com/paper/2005.08752/) (2020)
- [Deep Hyperspectral Prior: Denoising, Inpainting, Super-Resolution](https://mlsanity.com/paper/1902.00301/) (2019)
- [Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution](https://mlsanity.com/paper/2007.05230/) (2020)
- [Recent Advances and New Guidelines on Hyperspectral and Multispectral Image Fusion](https://mlsanity.com/paper/2008.03426/) (2020)

## Abstract (arXiv)

Deep learning-based (DL-based) hyperspectral image (HIS) super-resolution (SR) methods have achieved remarkable performance and attracted attention in industry and academia. Nonetheless, most current methods explored and learned the mapping relationship between low-resolution (LR) and high-resolution (HR) HSIs, leading to the side effect of increasing unreliability and irrationality in solving the ill-posed SR problem. We find, quite interestingly, LR imaging is similar to the mixed pixel phenomenon. A single photodetector in sensor arrays receives the reflectance signals reflected by a number of classes, resulting in low spatial resolution and mixed pixel problems. Inspired by this observation, this paper proposes a component-aware HSI SR network called UnmixingSR, in which the unsupervised HU as an auxiliary task is used to perceive the material components of HSIs. We regard HU as an auxiliary task and incorporate it into the HSI SR process by exploring the constraints between LR and HR abundances. Instead of only learning the mapping relationship between LR and HR HSIs, we leverage the bond between LR abundances and HR abundances to boost the stability of our method in solving SR problems. Moreover, the proposed unmixing process can be embedded into existing deep SR models as a plug-in-play auxiliary task. Experimental results on hyperspectral experiments show that unmixing process as an auxiliary task incorporated into the SR problem is feasible and rational, achieving outstanding performance. The code is available at