3-D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising

作者：Kaixuan Wei, Ying Fu, Hua Huang · 发表于：IEEE Transactions on Neural Networks and Learning Systems · 年份：2020 · DOI：10.1109/tnnls.2020.2978756 · 被引用次数：244 · 研究领域：Image and Signal Denoising Methods、Advanced Image Fusion Techniques、Remote-Sensing Image Classification

In this article, we propose an alternating directional 3-D quasi-recurrent neural network for hyperspectral image (HSI) denoising, which can effectively embed the domain knowledge-structural spatiospectral correlation and global correlation along spectrum (GCS). Specifically, 3-D convolution is utilized to extract structural spatiospectral correlation in an HSI, while a quasi-recurrent pooling function is employed to capture the GCS. Moreover, the alternating directional structure is introduced to eliminate the causal dependence with no additional computation cost. The proposed model is capable of modeling spatiospectral dependence while preserving the flexibility toward HSIs with an arbitrary number of bands. Extensive experiments on HSI denoising demonstrate significant improvement over the state-of-the-art under various noise settings, in terms of both restoration accuracy and computation time. Our code is available at https://github.com/Vandermode/QRNN3D.