用预训练的RGB去噪器修复高光谱图像，无需大量数据
Leveraging pretrained RGB denoisers for hyperspectral image restoration
- 通过投影映射复用冻结的RGB去噪器，轻量适配高光谱任务
- 在多个数据集上实现去噪、去模糊和超分辨率性能提升
- 适合缺乏高光谱数据的研究者快速部署高效修复模型
高光谱图像修复面临训练数据有限、传感器特性强和高光谱维度高等挑战，制约了鲁棒先验的学习。为此，本文提出一种极简训练、轻量级适配器，通过投影映射复用冻结的预训练RGB去噪器进行高光谱修复。方法对低维光谱投影进行去噪，并通过约束线性聚合重建高光谱立方体，同时保持即插即用兼容性和底层RGB去噪器的稳定性。在多个数据集上的去噪、去模糊和超分辨率实验均显著优于专用高光谱基线模型，验证了大规模RGB先验的强大可迁移性。
原文摘要 · Abstract (English)
Hyperspectral image restoration faces several challenges, including limited training data, strong sensor specificity, and high spectral dimensionality. These limitations hinder the learning of robust hyperspectral priors, motivating the reuse of priors learned from large-scale RGB data. In this work, we propose a minimally trained, lightweight adapter that repurposes frozen pretrained RGB denoisers for hyperspectral restoration through a projection mapping. The method denoises low-dimensional spectral projections and reconstructs the hyperspectral cube through constrained linear aggregation, while preserving plug-and-play compatibility and the stability properties of the underlying RGB denoiser. Experiments on denoising, deblurring, and super-resolution across multiple datasets demonstrate consistent improvements over hyperspectral-specific baselines, showing the strong transferability of large-scale RGB priors.
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