Hyperspectral image super-resolution reconstruction based on image partition and detail enhancement

Abstract

The hyperspectral image (HSI) super-resolution reconstruction has attracted much attention and been used widely in various study fields due to its low requirements on hardware in practice. However, most of the hyperspectral image super-resolution reconstruction studies apply one strategy for images with varying complexity of spatial information. This is not conducive to the improvement of Image processing efficiency and the extraction of complex details. Given the above, a new method named MSDESR (multilevel streams and detail enhancement) is proposed to reconstruct HSI by using partition reconstruction and detail enhancement. The MSDESR consists of a sub-map shunt block, a high-low-frequency information extraction with detail enhancement block, and a partition image reconstruction block. Firstly, the sub-map shunt block is designed to pre-classify hyperspectral images. The images are divided into complex and simple parts according to the spatial information distribution of the reconstructed sub-map. Secondly, the multiscale Retinex with detail enhancement algorithm is constructed to purify high-frequency noise-contaminated and enhance the image details by separating the samples into high- and low-frequency information. Finally, branching networks of different complexities are designed to reconstruct the images with high credibility and clear content. In this paper, datasets of QUST-1, Pavia University, Chikusei, the Washington DC Mal and XiongAn are applied in the experiments. The results show that MSDESR outperforms state-of-the-art CNN-based methods in terms of quantitative metrics, visual quality, and computational effort, with a 4.18% and 9.35% improvement in SRE and MPSNR metrics, and a 37% saving in FLOPs. Overall, the MSDESR performs well in hyperspectral image super-resolution reconstruction, which is time saving and preserves the details of spatial information.