Towards Memory-efficient Hyperspectral Image Reconstruction via Consistency Learning

Spectral reconstruction (SR) aims to recover high-quality hyperspectral images (HSIs) from more readily available RGB or multispectral images (MSIs). While supervised SR has shown promising results, it is hindered by the difficulty of collecting abundant, well-registered RGB-HSI or MSI-HSI pairs. Semi-supervised SR (Semi-SR) offers a more practical solution by exploiting plentiful RGBs/MSIs together with limited HSIs. However, existing Semi-SR approaches still suffer from cross-domain discrepancies, cross-modality inconsistency, and unreliable pseudo-labels. To tackle these challenges, we propose a Manifold-aware Teacher-Student Semi-SR (MTSSR) framework, which seamlessly integrates labeled and unlabeled domains through a teacher-student paradigm and memory-efficient consistency learning. At its core, a Flexible Cross-attention Spectral Reconstruction (FCSR) network extracts scene-related spatial cues via customized self-attention and models scene-agnostic priors through dynamic quantization, thereby enhancing spectral fidelity. Furthermore, a manifold-aware dimensionality analysis derives a latent space that jointly captures spatial and spectral structures across modalities. This enables a manifold-aware alignment loss to enforce cross-modality consistency and a manifold-aware contrastive loss to progressively refine pseudo-label reliability. In addition, we develop a Threshold-adjusted Memory Bank Update (TMBU) strategy, which generates reliable negative samples by storing network-driven representations instead of memory-consuming HSIs, significantly reducing memory consumption. Extensive experiments on three visual and two remote sensing benchmarks demonstrate that MTSSR consistently outperforms state-of-the-art SR methods, achieving robust and memory-efficient spectral reconstruction.