Spatial Constrained Hyperspectral Reconstruction from RGB Inputs Using Dictionary Representation

Reconstructing hyperspectral images from RGB inputs has gained great attention recently. In dictionary representation-based hyperspectral image reconstruction, dictionary representation is first carried out in RGB space and then dictionary reconstruction is conducted in hyperspectral space for per-pixel reconstruction. However, such work mainly focuses on spectral mapping from RGB space to hyperspectral space, ignoring physical distribution of objects in the image. In this paper, spatial context of pixels is used to improve the reconstruction performance. Specially, neighboring pixels are used to constrain the dictionary representation problem in RGB space, and the Simultaneous Orthogonal Matching Pursuit (SOMP) is used to improve the performance of hyperspectral reconstruction. Experimental results on two benchmark data sets demonstrate the superiority of the proposed technique..