Fusing a low spatial resolution hyperspectral image with a high spatial resolution multispectral image has become popular for generating a high spatial resolution hyperspectral image (HR-HSI). Most methods assume that the degradation information from high resolution to low resolution is known in spatial and spectral domains. Conversely, this information is often limited or unavailable in practice, restricting their performance. Furthermore, existing fusion methods still face the problem of insufficient exploration of the cross-interaction between the spatial and spectral domains in the HR-HSI, leaving scope for further improvement. This paper proposes an unsupervised Hybrid Network of Transformer and CNN (uHNTC) for blind HSI-MSI fusion. The uHNTC comprises three subnetworks: a transformer-based feature fusion subnetwork (FeafusFomer) and two CNN-based degradation subnetworks (SpaDNet and SpeDNet). Considering the strong multi-level spatio-spectral correlation between the desired HR-HSI and the observed images, we design a Multi-level Cross-feature Attention (MCA) mechanism in FeafusFormer. By incorporating the hierarchical spatio-spectral feature fusion into the attention mechanism in the transformer, the MCA globally keeps a high spatio-spectral cross-similarity between the recovered HR-HSI and observed images, thereby ensuring the high cross-interaction of the recovered HR-HSI. Subsequently, the characteristics of degradation information are utilized to guide the design of the SpaDNet and SpeDNet, which helps FeafusFormer accurately recover the desired HR-HSI in complex real-world environments. Through an unsupervised joint training of the three subnetworks, uHNTC recovers the desired HR-HSI without pre-known degradation information. Experimental results on three public datasets and a WorldView-2 images show that the uHNTC outperforms ten state-of-the-art fusion methods. Code available: https://github.com/Caoxuheng/HIFtool.

Recently, it has become popular to obtain a high spatial resolution hyperspectral image (HR-HSI) by fusing a low spatial resolution hyperspectral image (LR-HSI) with a high spatial resolution RGB image (HR-RGB). Existing HSI super-resolution methods are designed based on a known spatial degeneration. In practice, it is difficult to obtain correct spatial degradation, which restricts the performance of existing methods. Therefore, we propose a multi-stage scheme without employing the spatial degradation model. The multi-stage scheme consists of three stages: initialization, modification, and refinement. According to the angle similarity between the HR-RGB pixel and LR-HSI spectra, we first initialize a spectrum for each HR-RGB pixel. Then, we propose a polynomial function to modify the initialized spectrum so that the RGB color values of the modified spectrum are the same as the HR-RGB. Finally, the modified HR-HSI is refined by a proposed optimization model, in which a novel, to the best of our knowledge, spectral-spatial total variation (SSTV) regularizer is investigated to keep the spectral and spatial structure of the reconstructed HR-HSI. The experimental results on two public datasets and our real-world images demonstrate our method outperforms eight state-of-the-art existing methods in terms of both reconstruction accuracy and computational efficiency.