Toward Stable, Interpretable, and Lightweight Hyperspectral Super-Resolution

Wen-jin Guo, Weiying Xie, Kai Jiang, Yunsong Li, Jie Lei, Leyuan Fang

Abstract: 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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$120\times$</tex> model size reduction and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4300 \times$</tex> FLOPs reduction under the CAVE dataset. Code is available in https://github.com/WenjinGuo/DAEM.