Hyperspectral Image Denoising Based on Nonlocal Memory-Augmented Spectral Attention

In this paper, we propose an effective denoising method aimed at restoring clean images from hyperspectral images disturbed by multiple types of noise. Previously, data-based hyperspectral denoising methods mainly utilize adjacent spectral bands to assist in image restoration. However, due to the limited information provided by adjacent bands under the same level of noise interference, the performance of hyperspectral denoising algorithms is limited. In contrast, this paper introduces a cross-band non-local attention strategy that can supplement the spectral non-local similarity relationship of hyperspectral data. In addition, to alleviate the problem of different levels of noise interference in different bands, this paper uses memory-augmented attention to introduce global spectral information into each band, thereby fully utilizing inter band information to remove mixed noise. Furthermore, this paper uses multi-scale spatial information combined with spectral information to enhance denoising performance. The experiments conducted on both synthetic and real data have demonstrated the superiority of the proposed method over other methods.