PHASE: 通过对象到人体域适应的生理感知高光谱重建
PHASE: Physiology-Aware Hyperspectral Reconstruction via Object-to-Human Domain Adaptation
- The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
- South China University of Technology（华南理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对现有高光谱重建方法在生理成像中失效的问题，提出PHASE范式，通过生理通道重新解释和生理约束对齐，实现从对象到人体的域适应，仅需1.5%标注数据即可显著提升重建质量。
AI中文摘要：
尽管高光谱成像提供了无与伦比的无创生理洞察，但其笨重的硬件、缓慢的采集速度和监管负担严重限制了其临床可用性。一种自然的替代方案是从无处不在的RGB或CASSI测量中重建高光谱信息。然而，现有的为以对象为中心的场景开发的范式依赖于基于反射率的特征对齐，假设光谱相似性保持语义一致性。这一假设在生理成像中不成立，因为视觉上相似的RGB响应可能源于不同且纠缠的生理状态。这种不匹配促使从反射率对齐转向基于共享光-物质相互作用原理的生理感知表示学习——这一转变引入了来自跨通道语义偏移（C1）和基于RGB采集的不可逆信息丢失（C2）的基本挑战。因此，我们设计了PHASE，一种生理感知的高光谱重建范式，通过生理通道重新解释解耦跨通道生理语义，并通过生理约束对齐将重建限制在生理上合理的解，从根本上重新定义了对象到人体的迁移。在两种源到目标迁移协议下，PHASE仅需1.5%的标注监督，在SSIM上一致优于最先进方法最多+2.20，在SAM上最多-3.06。
英文摘要：
Although hyperspectral imaging offers unparalleled non-invasive physiological insight, its bulky hardware, slow acquisition, and regulatory burden severely limit its clinical availability. A natural workaround is to reconstruct hyperspectral information from ubiquitous RGB or CASSI measurements. However, existing paradigms, developed for object-centric scenes, rely on reflectance-based feature alignment, assuming that spectral similarity preserves semantic meaning. This assumption breaks down in physiological imaging, where visually similar RGB responses may arise from distinct and entangled physiological states. This mismatch motivates a shift from reflectance alignment to physiology-aware representation learning, grounded in shared light-matter interaction principles -- a shift that introduces fundamental challenges from cross-channel semantic shifts (C1) and irreversible information loss in RGB-based acquisition (C2). We therefore design PHASE, a physiology-aware hyperspectral reconstruction paradigm that fundamentally redefines object-to-human transfer by disentangling cross-channel physiological semantics via Physiological Channel Reinterpretation and restricting reconstruction to physiologically plausible solutions through Physiologically Constrained Alignment. Under two source-to-target transfer protocols, PHASE consistently outperforms state-of-the-art methods by up to +2.20 SSIM and -3.06 in SAM with merely 1.5% labeled supervision.
