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Meta-Collaborative Learning for Arbitrarily Scaled Hyperspectral Image Super-Resolution
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DOI：10.1109/TGRS.2025.3544253.png)
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Abstract
En 中文
Deep learning-based methods for hyperspectral image super-resolution (SR) have achieved significant success in recent years. These methods typically consist of feature extraction module (FEM) and upsampling module. However, due to structural limitations of the upsampling module, most current methods focus on training separate models for different scale factors, which ignores the exploration of potential feature interdependence among different scale factors. In response to these challenges, we introduce a novel framework, called meta-collaborative learning for arbitrarily scaled hyperspectral image super-resolution (MCArb). Specifically, MCArb integrates a collaborative learning framework with a meta-learning-based 3-D upsampling module (3DMetaUM) and a scale-aware feature adaptation module (SAFAM). It enables training multiple SR tasks at different scale factors within a single network at the same time. This strategy is able not only to process arbitrary-scale-factor SR for hyperspectral images but also to harness the latent feature interdependence among different scales. In this study, we applied the MCArb framework to transform three deep learning-based hyperspectral image SR networks to MCArb methods, resulting in significant performance enhancements across five hyperspectral datasets. These improvements showcase the proposed MCArb framework's ability to enhance feature extraction efficiency and capitalize on latent interscale correlations. This code is available at https://github.com/ShuangWu-XDU/MCArb_HSI_SR.
Keywords:
Collaborative learning
hyperspectral image
meta-learning
meta-learning
scale arbitrary
scale arbitrary
super-resolution (SR)
super-resolution (SR)
super-resolution (SR)
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