Hyperspectral Image Reconstruction for SD-CASSI Systems Based on Residual Attention Network

Abstract

Hyperspectral images contain both spatial and spectral information, which can be utilized to material identification. Therefore, they find significant advantages in object detection. Hyperspectral images are also believed to play an important part in geological survey and material classification. As the resolution of hyperspectral images increases, compressed sensing (CS) is proposed to reduce the data size, resulting in lower system latency. However, images after CS require reconstruction for further applications such as object detection. The idea of numerical optimization is adopted by conventional reconstruction algorithms. However, these algorithms are time-consuming in iteration. The efficiency and resulting image quality are also not satisfying. Therefore, deep neural networks (DNN) are expected to make better reconstruction algorithms. This paper proposes a novel reconstruction algorithm for hyperspectral images based on deep learning. The core idea is to apply a residual attention network. Firstly, convolution layers of different reception fields are applied to extract different features in hyperspectral images. Then the residual attention blocks satisfying the channel attention mechanism explore the inter-spectral correlation of hyperspectral images. Our proposed reconstruction model is tested to be effective and efficiency in experiments. Compared to three conventional algorithms, OMP, TwIST and GPSR, the proposed algorithm improves PSNR by over 8 db and reconstruction speed by 7 times. Moreover, the model achieves better reconstruction performance compared to a DNN-based model DNNnet.