Reconstructing Hyperspectral Images from RGB Inputs Based on Intrinsic Image Decomposition

Spectral super-resolution (SR), which generally reconstructs hyperspectral images (HSIs) from RGB inputs, has attracted lots of attention recently. In this paper, a spectral SR algorithm based on intrinsic image decomposition (IID) is proposed, in which RGB images are decomposed into reflectance images and shading images to fully explore RGB features for HSI reconstruction. Considering that features of the reflectance image are only related to the material of objects, the sparsity of material reflectivity is used to reconstruct the reflectance image of HSI. Moreover, an convonlutional neural network (CNN) is constructed to reconstruct shading parts of HSI. Finally, these two reconstructed results are fused to generate the high spectral resolution HSI and an enhancement network is also designed to further improve the recontruction performance. Experimental results with two benchmark datasets, ICVL and CAVE, demonstrate that the performance of the proposed algorithm is superior to several state-of-the-art spectral SR algorithms.