Attention-Based Octave Network for Hyperspectral Image Denoising

Inevitable corruption and degeneration make the performance of subsequent high-level semantic tasks in hyperspectral images (HSIs) unsatisfactory. Despite that many denoising methods have been proposed, significant room for improvement still remains. To better suppress noise and preserve the HSI spatial–spectral structure, we propose an attention-based Octave dense network. A separable spectral feature extraction module is introduced to extract the spatial–spectral features consistent with the structure prior. The extracted features are fine-tuned by the attention module in both channel and spatial domains; then, several dense denoising blocks are elaborately employed to focus on noise feature learning; in order to focus on high-frequency features, which usually have more noise information, we introduce the Octave kernel to implement these blocks. Experiments based on simulated and real-world noisy images demonstrate that the proposed method outperforms the existing traditional and learning-based methods in both quantitative evaluations and visual effects, benefiting the subsequent classification task. In addition, the effectiveness of each module is proven by ablation experiments. Our source code is made available at: https://github.com/LbzSteven/AODN.