Multi-task attentional u-net for hyperspectral image denoising

Hyperspectral image (HSI) denoising is a critical preprocessing step for ensuring the usability of HSIs. However, current deep learning-based approaches still struggle with modeling the global spectral correlation among bands, which is crucial for high-quality denoising. To address this issue, we introduce a multitask attention module and embed it into a U-Net architecture to yield a multitask attentional U-Net (MTA-Net) for HSI denoising. The module enforces all bands to focus on the same region by sharing the same attention map across all bands. This ensures that all bands capture the same image structure, effectively modeling the global spectral correlation. Experimental results demonstrate that the proposed MTA-Net achieves state-of-the-art performance on both synthetic and real-world data.