Spectral Image Reconstruction Using Recovered Basis Vector Coefficients

Spectral imaging plays a crucial role in various fields, including remote sensing, medical imaging, and material analysis, but it often requires specialized and expensive equipment, making it inaccessible to many. Its application is also limited by the interdependent constraints of temporal, spatial, and spectral resolutions. In order to address these issues, and thus, obtain high-quality spectral images in a time-efficient and affordable manner, we proposed one two-step method for spectral image reconstruction from easily available RGB images under the down-sampling schemes. Specifically, we investigated how RGB values characterize spectral reflectance and found that, compared to the intuitive and straightforward RGB images themselves, their corresponding basis vector coefficients can represent the prior information of spectral images more explicitly and are better suited for spectral image reconstruction tasks. Thus, we derived one data-driven algebraic method to recover the corresponding basis vector coefficients from RGB images in an analytical form and then employed one CNN-based neural network to learn the patch-level mapping from the recovered basis vector coefficients to spectral images. To evaluate the effect of introducing the basis vector coefficient recovery step, several CNNs which typically perform well in spectral image reconstruction are chosen as benchmarks to compare the variation in reconstruction performance. Experimental results on a large public spectral image dataset and our real-world dataset demonstrate that compared to the unaltered version, those CNNs guided by the recovered basis vector coefficients can achieve significant performance improvement in the reconstruction accuracy. Furthermore, this method is plug-and-play, with very little computational performance consumption, thus maintaining a high speed of calculation.