# Degradation Alchemy: Self-Supervised Unknown-to-Known Transformation for Blind Hyperspectral Image Fusion

arXiv: https://arxiv.org/abs/2503.14892 · HTML: https://mlsanity.com/paper/2503.14892/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 69 references that are in the map.
- First posted on arXiv 19 Mar 2025.

## Builds on (most cited first, 16 of 16)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [Deep Learning for Hyperspectral Image Classification: An Overview](https://mlsanity.com/paper/1910.12861/) (2019)
- [A convex formulation for hyperspectral image superresolution via subspace-based regularization](https://mlsanity.com/paper/1411.4005/) (2014)
- [Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery](https://mlsanity.com/paper/2005.08752/) (2020)
- [Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net](https://mlsanity.com/paper/1901.03281/) (2019)
- [Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution](https://mlsanity.com/paper/2109.02079/) (2021)
- [HSR-Diff:Hyperspectral Image Super-Resolution via Conditional Diffusion Models](https://mlsanity.com/paper/2306.12085/) (2023)
- [Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution](https://mlsanity.com/paper/2007.05230/) (2020)
- [Hyperspectral and Multispectral Image Fusion based on a Sparse Representation](https://mlsanity.com/paper/1409.5729/) (2014)
- [Guided Deep Decoder: Unsupervised Image Pair Fusion](https://mlsanity.com/paper/2007.11766/) (2020)
- [Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network](https://mlsanity.com/paper/1706.05249/) (2017)
- [Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning](https://mlsanity.com/paper/2006.10300/) (2020)
- [Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation](https://mlsanity.com/paper/1710.05705/) (2017)
- [Spatial-Spectral Fusion by Combining Deep Learning and Variation Model](https://mlsanity.com/paper/1809.00764/) (2018)
- [Unsupervised Hyperspectral and Multispectral Image Blind Fusion Based on Deep Tucker Decomposition Network with Spatial-Spectral Manifold Learning](https://mlsanity.com/paper/2409.09670/) (2024)

## Abstract (arXiv)

Hyperspectral image (HSI) fusion is an efficient technique that combines low-resolution HSI (LR-HSI) and high-resolution multispectral images (HR-MSI) to generate high-resolution HSI (HR-HSI). Existing supervised learning methods (SLMs) can yield promising results when test data degradation matches the training ones, but they face challenges in generalizing to unknown degradations. To unleash the potential and generalization ability of SLMs, we propose a novel self-supervised unknown-to-known degradation transformation framework (U2K) for blind HSI fusion, which adaptively transforms unknown degradation into the same type of degradation as those handled by pre-trained SLMs. Specifically, the proposed U2K framework consists of: (1) spatial and spectral Degradation Wrapping (DW) modules that map HR-HSI to unknown degraded HR-MSI and LR-HSI, and (2) Degradation Transformation (DT) modules that convert these wrapped data into predefined degradation patterns. The transformed HR-MSI and LR-HSI pairs are then processed by a pre-trained network to reconstruct the target HR-HSI. We train the U2K framework in a self-supervised manner using consistency loss and greedy alternating optimization, significantly improving the flexibility of blind HSI fusion. Extensive experiments confirm the effectiveness of our proposed U2K framework in boosting the adaptability of five existing SLMs under various degradation settings and surpassing state-of-the-art blind methods.