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Compressive Hyperspectral Image Reconstruction Based on Spatial–Spectral Residual Dense Network

A spatial–spectral residual dense network-based compressive hyperspectral image (HSI) reconstruction method is proposed in this letter. The proposed method contains two networks: residual dense network for hyperspectral image reconstruction (RDNHIR) and spectral difference reconstruction network (SDRN). The RDNHIR network can extract the local features and global hierarchical features by cascading features of all residual dense blocks (RDBs). Then, SDRN takes full advantage of the strong correlation between spectral adjacent bands to better preserve the spectral feature of HSI. Finally, the adjacent spectral difference regularization is introduced into the loss function to further improve the performance. The experimental results show that the proposed method has better reconstruction quality than other state-of-the-art reconstruction methods, especially in the spectral domain.