Heterospectral Structure Compensation Sampling for Hyperspectral Fusion Computational Imaging

Existing hyperspectral fusion computational imaging methods primarily rely on using high-resolution multispectral images (HRMSI) to provide spatial details for low-resolution hyperspectral images (LRHSI), thereby enabling the reconstruction of hyperspectral images. However, these methods are often limited by the low spectral resolution of the HRMSI, making the sampled tensors unable to provide effective information for the LRHSI in a finer spectral range. To achieve more accurate computational imaging results, we propose a Heterospectral Structure Compensation Sampling (HSC-sampling) mechanism. Unlike traditional spatial sampling methods, which directly calculate the interpolation between adjacent pixels, this mechanism analyzes the structural complementarity among different bands in LRHSI. It utilizes the information from other bands to compensate for the missing details in the current band. Additionally, a novel Multi-phase Mixed Modeling (M2M) approach is designed, expanding the model's analytical capabilities into multiple phases to accommodate the high-dimensional nature of HSI data. Specifically, it extracts fusion features from three phases and organizes the generated features along with the input features into a multi-variate mixed cube based on phase relationships, thereby capturing feature correlations across different phases. Based on the HSC-sampling mechanism and the M2M approach, we construct a Merging Residual Concatenation (MRC) hyperspectral fusion computational imaging network. Compared to other state-of-the-art methods, this network achieves significant improvements in fusion performance across multiple datasets. Moreover, the effectiveness of the HSC-sampling mechanism has been demonstrated in various hyperspectral imaging tasks. Code is available at: https://github.com/1318133/HSC-Sampling.