Hyperspectral Image Super-Resolution Based on Multiscale Mixed Attention Network Fusion

Abstract

Hyperspectral images (HSIs) contain rich spectral information and have great application value. However, due to various hardware limitations, the spatial resolution of HSIs acquired by the sensor is low. HSI super-resolution (SR) attracts much attention to improve spatial quality. In this letter, a single HSI SR method based on network fusion is proposed. Our method includes the SR network part and fusion part. In the SR network part, we construct 3-D multiscale mixed attention networks (3-D-MSMANs) by cascading 3-D multiscale mixed attention block (3-D-MSMAB) to restore high-resolution HSIs. 3-D-MSMAB consists of the 3-D Res2net module and the mixed attention module. 3-D Res2net module is a simple and effective multiscale method. The mixed attention module is proposed by combining the first- and second-order statistics of features. In addition, we use the mutual learning loss between 3-D-MSMAN so that they can learn from each other. In the fusion part, the fusion module is designed to merge the output of each 3-D-MSMAN. Our method can achieve good results in both simulated and real SR experiments. Code is available at https://github.com/LYT-max/Mixed-Attention-for-HSI-SR .