PMFF-SRNet: A progressive multi-feature fusion network for hyperspectral image reconstruction

Hyperspectral imaging (HSI) contains abundant spatial and spectral cues, making it advantageous for a wide range of applications, including Earth observation, medicine, as well as agricultural analysis. However, conventional HSI systems are often constrained by high costs, limited acquisition efficiency, and weak adaptability to dynamic scenes. To mitigate these limitations, we propose a progressive multi-feature fusion model, termed PMFF-SRNet, for RGB-to-hyperspectral reconstruction. The proposed model progressively recovers spectral information through multiple stages, which helps reduce spectral redundancy while improving reconstruction efficiency. Furthermore, a Local–Global Spectral Attention (LGSA) module is employed to model spectral features at different granularities, where grouped self-attention focuses on local band interactions while spectral order information contributes to long-range dependency modeling. In addition, a Discrete Wavelet Attention (DWA) module is incorporated into the skip connections to enhance texture and edge restoration by exploiting the multi-scale characteristics of wavelet transforms. Results obtained on the NTIRE benchmarks indicate that PMFF-SRNet achieves competitive reconstruction performance across multiple evaluation metrics, while maintaining a lightweight and computationally efficient architecture. These findings demonstrate the strong potential of PMFF-SRNet for practical hyperspectral reconstruction tasks.