A Frequency-Separated 3D-CNN for Hyperspectral Image Super-Resolution

Considering the limitations such as cost, it is of great significance to use super-resolution methods to improve image spatial quality in the field of hyperspectral remote sensing. Due to little dependence on auxiliary information which is difficult to obtain, i.e., multispectral images and natural images, methods based on single-frame are generally considered to have good flexibility and application value. In this paper, a three-dimensional convolutional neural network with three branches combined with an analytical method is proposed, achieving better SR quality and suppressing spectral distortion as well. Firstly, the wavelet transformation is introduced to decompose the hyperspectral image into a variety frequency of components effectively and reversibly. Then, these components are fed into different three-dimensional convolutional branches respectively. Finally, hyperspectral images with high resolution are obtained by dimension amplification, detail reconstruction and inverse wavelet transformation. The presence of frequency separation and the architecture of our model having different branches designed according to frequency make it better than comparable approaches. The method proposed in this paper not only combines the high efficiency of analytical method and the flexibility of neural network, but inhibits the influence of spectral distortion as well. Compared with the state-of-art methods on real space-based hyperspectral image datasets, the effectiveness of the proposed method is demonstrated.