Deep learning networks based on CNNs or transformers have made progress in spectral reconstruction (SR). However, many methods focus solely on feature extraction, overlooking the interpretability of network design. Additionally, models exclusively based on CNNs or transformers may lose other prior information, sacrificing reconstruction accuracy and robustness. In this paper, we propose a novel Unmixing-Guided Convolutional Transformer Network (UGCT) for interpretable SR. Specifically, transformer and ResBlock components are embedded in Paralleled-Residual Multi-Head Self-Attention (PMSA) to facilitate fine feature extraction guided by the excellent priors of local and non-local information from CNNs and transformers. Furthermore, the Spectral–Spatial Aggregation Module (S2AM) combines the advantages of geometric invariance and global receptive fields to enhance the reconstruction performance. Finally, we exploit a hyperspectral unmixing (HU) mechanism-driven framework at the end of the model, incorporating detailed features from the spectral library using LMM and employing precise endmember features to achieve a more refined interpretation of mixed pixels in HSI at sub-pixel scales. Experimental results demonstrate the superiority of our proposed UGCT, especially in the grss_d f c_2018 dataset, in which UGCT attains an RMSE of 0.0866, outperforming other comparative methods.

The persistent challenge of acquiring precisely coregistered RGB-hyperspectral image (HSI) pairs has significantly impeded the practical deployment of current data-driven Hyperspectral Image Generation (HIG) networks in engineering applications. Gleichzeitig, the ill-posed nature of the aligning constraints, compounded with the complexities of mining cross-domain features, also hinders the advancement of unpaired HIG (UnHIG) tasks. In this paper, we conquer these challenges by reformulating the UnHIG through Range-Null Space Decomposition (RND), modeling range-space feature interaction and null-space compensations. Specifically, the introduced contrastive learning effectively aligns the geometric and spectral distributions of unpaired data by building the interaction of range space, considering the consistent feature in degradation process. Our Dual-Dimensional Contrastive Prior Module (DCPM) captures mutual information within RGB and HSI domains of a single scene while modeling relationships between internal representations across different scenes, thereby constructing comprehensive cross-domain constraints. Furthermore, the Gabor kernel-based multi-head self-attention (G-MSA) adaptively separates high-frequency components, guiding subsequent modules to concentrate on relevant frequency intervals for target objects. Then, we propose a novel Non-uniform Kolmogorov-Arnold Networks (Nukes) to exhaustively excavate null-space components(degraded/high-frequency representations) through dual-domain frequency mapping. The proposed method was evaluated on three established datasets: NTIRE 2020 ’Clean’ track, NTIRE 2022, CAVE and Grss_dfc_2018. To assess real-world applicability, ratio experiments were conducted by changing the proportion of RGB and HSI. These experiments demonstrate that our approach achieves state-of-the-art performance in UnHIG.