SMDe: enhancing hyperspectral image denoising through a spatial-spectral modulated network

Hyperspectral image denoising is crucial for restoring effective information from noisy data and plays a significant role in various downstream applications. Deep learning based methods have become the mainstream research direction due to their ability to handle complex noise. However, the spatial feature extraction of most existing methods is not comprehensive enough, and the adoption of a fixed spectral reconstruction mode does not fully utilize the spectral information of the original image. To address these issues, we propose a Spatial-Spectral Modulated Network for hyperspectral image denoising (SMDe). It consists of a spatial feature extraction network and a spectral modulation module. For the spatial feature extraction, we construct a hybrid network that combines Mamba and Transformer layers, which can effectively capture global and local spatial information. For the spectral modulation module, we design a discrimination strategy that adaptively preserves or reconstructs spectral information from the original image spectra. This information is then used to modulate spatial features, enhancing the spectral fidelity of the denoised image. Experiments on synthetic datasets show that SMDe outperforms other advanced methods in most noisy scenarios, not only restoring image details but also maintaining excellent spectral consistency. In cross-dataset and real-data evaluations, the denoising results of SMDe also demonstrated strong competitiveness.