Enhanced 3D Convolution for Hyperspectral Image Super-Resolution

Three-dimensional (3D) convolution is well-suited for volumetric data exploration, and therefore it has great potential in spatial-spectral feature learning to promote hyperspectral image super-resolution (HSI SR). However, 3D convolution is computationally expensive, and this is especially true when it operates on the high spectral dimensionality. In this paper, we design the enhanced 3D (E3D) convolution, an efficient form of spatial-spectral convolution. The standard 3D convolution is factorized into sequential spatial and spectral components. And the novel lightweight spatial and spectral squeeze-and-excitation modules are incorporated to corresponding components, respectively. As such, E3D convolution can largely reduce the computational complexity and extract effective spatial-spectral features with the holistic information. We further construct a fully 3D convolutional network (E3DN) with the proposed E3D convolution. The additional global residual learning and share-source skip connections can achieve spectral mapping and facilitate feature propagation. The simulated and real experiments demonstrate the accuracy and performance advantages of E3DN.