SPECAT: SPatial-spEctral Cumulative-Attention Transformer for High-Resolution Hyperspectral Image Reconstruction

Compressive spectral image reconstruction is a critical method for acquiring images with high spatial and spectral resolution. Current advanced methods, which involve designing deeper networks or adding more self-attention modules, are limited by the scope of attention modules and the irrelevance of attentions across different dimensions. This leads to difficulties in capturing non-local mutation features in the spatial-spectral domain and results in a significant parameter increase but only limited performance improvement. To address these issues, we propose SPECAT, a SPatial-spEctral Cumulative-Attention Transformer designed for high-resolution hyperspectral image reconstruction. SPECAT utilizes Cumulative-Attention Blocks (CABs) within an efficient hierarchical framework to extract features from non-local spatial-spectral details. Furthermore, it employs a projection-object Dual-domain Loss Function (DLF) to integrate the optical path constraint, a physical aspect often overlooked in current methodologies. Ultimately, SPECAT not only significantly enhances the reconstruction quality of spectral details but also breaks through the bottleneck of mutual restriction between the number of parameters and the accuracy of reconstruction in existing algorithms. Our experimental results demonstrate the superiority of SPECAT, achieving 40.3 dB in hyperspectral reconstruction benchmarks, outperforming the state-of-the-art (SOTA) algorithms by 1.2 dB while using only 5% of the network parameters and 10% of the computational cost.