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 de-signing deeper networks or adding more self-attention mod-ules, 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 signif-icant parameter increase but only limited performance im-provement. To address these issues, we propose SPECAT, a SPatial-spEctral Cumulative-Attention Transformer de-signed 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. Ulti-mately, SPECAT not only significantly enhances the reconstruction quality of spectral details but also breaks through the bottleneck of mutual restriction between the cost and accuracy in existing algorithms. Our experimental re-sults demonstrate the superiority of SPECAT, achieving 40.3 dB in hyperspectral reconstruction benchmarks, out-performing 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. The code is available at https://github.com/THU-luvisionISPECAT.