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Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution
Jin-Fan Hu, Ting-Zhu Huang, Liang-Jian Deng
TL;DR
Hyperspectral super-resolution must recover spatial detail despite an imaging trade-off between spatial and spectral resolution, while CNN methods mainly capture local information. Fusformer fuses LR-HSI and HR-MSI with transformer self-attention and residual estimation, achieving strong benchmark results with few parameters.
Problem
Imaging systems cannot simultaneously provide high spatial and spectral resolution, and CNN-based methods have limited receptive fields for global information.
Method
Fusformer uses transformer self-attention to fuse LR-HSI and HR-MSI while estimating residuals rather than reconstructing the whole HR-HSI.
Results
Fusformer achieves the best results on almost every quality index in the CAVE and Harvard datasets while using only 0.1 million parameters.
Takeaways & Limitations
The proposed transformer framework offers a simple, lightweight approach for hyperspectral image super-resolution and can support future research.
Takeaways & Limitations
In generalization experiments, Fusformer is satisfying, but its ERGAS is not the smallest.
Abstract
from arXiv · showhide
Hyperspectral image has become increasingly crucial due to its abundant spectral information. However, It has poor spatial resolution with the limitation of the current imaging mechanism. Nowadays, many convolutional neural networks have been proposed for the hyperspectral image super-resolution problem. However, convolutional neural network (CNN) based methods only consider the local information instead of the global one with the limited kernel size of receptive field in the convolution operation. In this paper, we design a network based on the transformer for fusing the low-resolution hyperspectral images and high-resolution multispectral images to obtain the high-resolution hyperspectral images. Thanks to the representing ability of the transformer, our approach is able to explore the intrinsic relationships of features globally. Furthermore, considering the LR-HSIs hold the main spectral structure, the network focuses on the spatial detail estimation releasing from the burden of reconstructing the whole data. It reduces the mapping space of the proposed network, which enhances the final performance. Various experiments and quality indexes show our approach's superiority compared with other state-of-the-art methods.
1 Introduction
Hyperspectral imaging provides rich spectral information but cannot simultaneously achieve high spatial and spectral resolution, motivating fusion of LR-HSI and HR-MSI. Fusformer uses transformer self-attention and residual estimation to address CNN limitations while reducing mapping complexity.
- HSIs provide abundant spectral information, but imaging systems trade off spatial resolution against spectral resolution.
- Fusing LR-HSI spectral information with HR-MSI spatial resolution can generate the desired HR-HSI.
- Traditional factorization-based methods often require known or estimated B and R, which are difficult to obtain in practice.
- CNN-based fusion methods outperform many traditional methods but remain limited by convolution’s insufficient information extraction ability.
- Fusformer applies transformer self-attention to model global relationships and estimates residuals in a smaller mapping space instead of reconstructing HR-HSI directly.
- The authors report few parameters and light computation, making the approach practical and leaving a simple architecture for future research.
2 Network Architecture
Fusformer combines upsampled LR-HSI and HR-MSI inputs, embeds pixel-wise spectral-spatial information, and processes it with a transformer to model global relationships. A reshape-and-refine stage estimates residuals that are added to the upsampled LR-HSI.
- Architecture motivation: CNNs rely on localized convolution kernels, while Fusformer uses transformer attention to consider relationships among image features globally.The architecture is motivated by limited global information in regular convolution operations.
- Input representation: The network concatenates upsampled LR-HSI and HR-MSI along the spectral dimension to form a data cube containing spectral and spatial information.The resulting cube has dimensions H × W × (S+s).
- Input representation: Fusformer unfolds the data cube into pixel vectors, preserving each pixel's spectral structure and spatial information for transformer processing.Unlike patch-based reshaping, each matrix row represents one image pixel.
- Transformer module: The transformer uses encoder and decoder components with layer normalization and multi-head attention to capture global relationships.The attention mechanism assigns differentiated importance levels to values through softmax.
- Reshape and refinement: After transformer processing, learned features are reshaped into a 3D tensor, refined into a residual, and added to the upsampled LR-HSI to produce the output.The refine module produces a residual with S spectral bands.
3 Experiment Results
Fusformer is evaluated against traditional and deep learning methods on CAVE and Harvard datasets, with quantitative, visual, generalization, and residual-learning analyses. The reported results show strong performance with few parameters, while ERGAS is not the smallest in generalization testing.
- Benchmark Setup: Fusformer is compared with four traditional and four deep learning-based methods on the CAVE and Harvard hyperspectral datasets.Both datasets contain hyperspectral images with 31 spectral channels; CAVE images are 512 × 512, while Harvard images are cropped to 1000 × 1000.
- Quantitative Results: Fusformer obtains the best results on almost every QI across the quantitative comparisons and uses only 0.1 million parameters.The comparisons are reported for the CAVE and Harvard datasets.
- Visual Results: Fusformer outperforms the other benchmark methods visually, with the darkest residuals in the shown CAVE and Harvard examples.Figure 2 presents true pseudo-color images, LR-HSI inputs, fused products, residuals, and close-ups for feather and window samples.
- Generalization Ability: In generalization testing, Fusformer remains satisfying, although ERGAS is not the smallest; HSRnet performs closely but uses many more parameters.The passage frames generalization as important because deep learning methods can perform poorly on examples differing from their training data.
- Ablation Study: Adding the upsampled LR-HSI through residual learning is reported as vital, because its rough information boosts performance and strengthens stability.The ablation compares the proposed method with and without the residual learning strategy on the CAVE dataset.
4 Conclusion
Fusformer uses a transformer to incorporate global information for hyperspectral image super-resolution while estimating residuals with few parameters. The authors describe it as simple and effective, and identify the framework as a basis for future research.
- Fusformer uses a transformer to consider global information instead of the local information captured within limited convolutional receptive fields.
- The method estimates residuals rather than reconstructing the whole high-resolution hyperspectral image, reducing the mapping space.
- Fusformer is described as simple yet effective and contains few parameters.
- The proposed framework is presented as a foundation for future research into its potential.