Multi-Frequency Consistency and Spectral-Basis Caching for Spectral-Aware Staining

Virtual staining enables the generation of biologically informative microscopy images without physical staining procedures. However, conventional evaluation and training objectives may overlook fine-grained biological structures. Downstream evaluation is more biologically relevant but requires task-specific annotations, predictive models, or expert assessment, limiting the scalability of downstream evaluation. Meanwhile, diffusion-based virtual staining relies on iterative denoising and can incur substantial inference cost. We propose Spectral-Aware Staining, a spectral-guided framework for virtual staining that improves staining quality assessment and diffusion acceleration. First, we introduce Multi-Frequency Spectral-Aware Consistency (MSAC), a spectral-aware consistency metric that evaluates virtual staining quality by comparing retained multi-frequency spectral subspaces. By enforcing multi-frequency consistency, the MSAC better preserves biologically relevant morphology. Second, we propose Spectral-Basis Cache (SBCache), a structure-aware caching strategy for diffusion inference. SBCache estimates denoising redundancy in a learned graph-spectral space and enables reuse of intermediate computations while achieving a favorable quality–efficiency trade-off. Both MSAC and SBCache share the same trained neural spectral decomposer, which predicts ordered graph-spectral bases from feature graphs and replaces computationally expensive eigendecomposition with efficient network inference. Experiments on public microscopy benchmarks and virtual-staining frameworks demonstrate that MSAC more effectively evaluates the quality of generated stains and that Spectral-Aware Staining generates high-quality stained images more efficiently.