SS^3L: Self-Supervised Spectral-Spatial Subspace Learning for Hyperspectral Image Denoising

Hyperspectral imaging (HSI) systems often suffer from complex noise degradation during the imaging process, significantly impacting downstream applications. Deep learning-based methods, though effective, rely on impractical paired training data, while traditional model-based methods require manually tuned hyperparameters and lack generalization. To address these issues, we propose SS³L (Self-supervised Spectral-Spatial Subspace Learning), a novel HSI denoising framework that requires neither paired data nor manual tuning. Specifically, we introduce a self-supervised spectral-spatial paradigm that learns noisy features from noisy data rather than paired training data, based on spatial geometric symmetry and spectral local consistency constraints. To avoid manual hyperparameter tuning, we propose an adaptive rank subspace representation and a loss function designed based on the collaborative integration of spectral and spatial losses via noise-aware spectral-spatial weighting, guided by the estimated noise intensity. These components jointly enable a dynamic trade-off between detail preservation and noise reduction under varying noise levels. The proposed SS$^3$L embeds noise-adaptive subspace representations into the dynamic spectral-spatial hybrid loss constrained network, enabling cross-sensor denoising through prior-informed self-supervision. Experimental results demonstrate that SS$^3$L effectively removes noise while preserving both structural fidelity and spectral accuracy under diverse noise conditions. The source code will be available at {https://github.com/yinhuwu/SS3L}