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 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 (AODN). Separatable 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 Octave kernel to implement these blocks. Experiments based on simulated and real-world noisy images demonstrate that the proposed method outperforms 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.