Modified SSR-NET: A Shallow Convolutional Neural Network for Efficient Hyperspectral Image Super-Resolution - Avagyan, Shushik; Katkovnik, Vladimir; Egiazarian, Karen (Frontiers in Remote Sensing, 2022)
A fast and shallow convolutional neural network is proposed for hyperspectral image super-resolution inspired by Spatial-Spectral Reconstruction Network (SSR-NET). The feature extraction ability is improved compared to SSR-NET and other state-of-the-art methods, while the proposed network is also shallow. Numerical experiments show both the visual and quantitative superiority of our method. Specifically, for the fusion setup with two inputs, obtained by $32 ×$ spatial downsampling for the low-resolution hyperspectral (LR HSI) input and $25 ×$ spectral downsampling for high-resolution multispectral (HR MSI) input, a significant improvement of the quality of super-resolved HR HSI over 4 dB is demonstrated as compared with SSR-NET. It is also shown that, in some cases, our method with a single input, HR MSI, can provide a comparable result with that achieved with two inputs, HR MSI and LR HSI.