A spatial constraint and deep learning based hyperspectral image super-resolution method

Abstract

The image super-resolution (SR) technique, which aims at reconstructing a high-resolution (HR) image from a single low-resolution (LR) image, is a classical problem in computer vision. Limited by the imaging hardware, the spatial resolution of a hyperspectral images (HSI) is usually very coarse. Meanwhile, the spectral information of the HSI is extremely important for its applications and cannot be severely distorted. This paper presents a spatial constraint (SCT) strategy with combination of a deep learning method for HSI SR. The SCT strategy restraints the LR HSI generated by the reconstructed HR HSI should be spatially close to the input LR HSI. The deep learning method learns an end-to-end mapping between the spectral difference of the LR HSI and that of the HR HSI. The mapping is represented as a deep convolutional neural network (CNN). The CNN learned spectral difference is utilized to super-resolve the LR HSI while preserve the important spectral information of the desired HR HSI. Experiments have been conducted on three databases that contains both indoor scenes and outdoor scenes. Comparative analyses have verified the effectiveness of the overall method.