Adaptive Heterogeneity-Aware Tensor Decomposition for Hyperspectral Image Denoising.

Hyperspectral image denoising must reconcile global spectral coherence with spatially heterogeneous scene content. This paper presents Adaptive Heterogeneity-Aware Tensor Decomposition (AHTD), a refinement module that augments a global Tucker initialization with heterogeneity-guided local tensor shrinkage. The method estimates a global spectral subspace, detects heterogeneous regions via combined variance and edge analysis, and applies adaptive weighted singular-value shrinkage modulated by regional patch complexity. To isolate the effect of the heterogeneity-aware selection mechanism itself, experiments employ a controlled internal ablation protocol comparing three pipeline variants under identical noise realizations, ranks, and parameter settings on the Pavia_80, Indian Pines corrected, and Salinas corrected benchmarks under synthetic mixed noise (σ=0.03 Gaussian with stripe and impulse noise): a global Tucker baseline, a dense full-local refinement variant, and the proposed selective AHTD. AHTD achieves peak signal-to-noise ratio (PSNR) gains of 0.35-0.40 dB over the global Tucker baseline while maintaining or improving structural similarity index (SSIM), spectral angle mapper (SAM), and relative dimensionless global error in synthesis (ERGAS), at approximately threefold lower runtime than dense full-local refinement, demonstrating its value as a computationally efficient, interpretable refinement stage for tensor-based hyperspectral processing. We note that matched comparisons against externally published denoising methods are not included in this study; these results establish the benefit of heterogeneity-aware selective refinement within the proposed Tucker-based pipeline.