Hybrid 2-D–3-D Deep Residual Attentional Network With Structure Tensor Constraints for Spectral Super-Resolution of RGB Images

RGB image spectral super-resolution (SSR) is a challenging task due to its serious ill-posedness, which aims at recovering a hyperspectral image (HSI) from a corresponding RGB image. In this article, we propose a novel hybrid 2-D-3-D deep residual attentional network (HDRAN) with structure tensor constraints, which can take fully advantage of the spatial-spectral context information in the reconstruction progress. Previous works improve the SSR performance only through stacking more layers to catch local spatial correlation neglecting the differences and interdependences among features, especially band features; different from them, our novel method focuses on the context information utilization. First, the proposed HDRAN consists of a 2D-RAN following by a 3D-RAN, where the 2D-RAN mainly focuses on extracting abundant spatial features, whereas the 3D-RAN mainly simulates the interband correlations. Then, we introduce 2-D channel attention and 3-D band attention mechanisms into the 2D-RAN and 3D-RAN, respectively, to adaptively recalibrate channelwise and bandwise feature responses for enhancing context features. Besides, since structure tensor represents structure and spatial information, we apply structure tensor constraint to further reconstruct more accurate high-frequency details during the training process. Experimental results demonstrate that our proposed method achieves the state-of-the-art performance in terms of mean relative absolute error (MRAE) and root mean square error (RMSE) on both the “clean” and “real world” tracks in the NTIRE 2018 Spectral Reconstruction Challenge. As for competitive ranking metric MRAE, our method separately achieves a 16.06% and 2.90% relative reduction on two tracks over the first place. Furthermore, we investigate HDRAN on the other two HSI benchmarks noted as the CAVE and Harvard data sets, also demonstrating better results than state-of-the-art methods.