高光谱扩散等变成像（HyDiff-EI）：一种用于高光谱图像修复的自监督框架
Hyperspectral Diffusion Equivariant Imaging (HyDiff-EI): A Self-supervised Framework for Hyperspectral Image Inpainting
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中文总结 AI 辅助
提出HyDiff-EI自监督框架，将扩散建模与等变先验结合，针对高光谱图像修复，在Chikusei等真实数据集上表现优于现有算法，适配标注数据有限的遥感场景。
中文摘要 AI 辅助
本文提出了一种用于解决高光谱图像（HSI）修复问题的新型高光谱扩散等变成像（HyDiff-EI）框架。与依赖大规模预训练的传统扩散方法不同，HyDiff-EI是一种测试时优化框架，可直接从单幅受损的HSI采集数据中学习，这使其对不同传感器配置具有灵活性，尤其适用于大型标注高光谱数据集有限的实际遥感场景。为解决无监督修复的不适定性，我们在扩散过程中嵌入等变一致性约束，通过利用HSI固有的几何对称性和内在特性，HyDiff-EI弥合了生成式扩散建模与自洽物理先验之间的差距。我们的实验表明，将扩散建模与等变先验相结合可显著提升噪声鲁棒性和泛化能力。在Chikusei、Botswana和EMIT等真实世界数据集上的大量实验证明，无论在无噪声还是有噪声的情况下，HyDiff-EI都比现有自监督算法和基于扩散的算法提供了显著更优的修复质量。
英文摘要
A novel Hyperspectral diffusion Equivariant Imaging (HyDiff-EI) framework for solving the hyperspectral image (HSI) inpainting problem has been presented here. Unlike conventional diffusion-based methods that rely on large-scale pretraining, HyDiff-EI is a test-time optimization framework that learns directly from a single corrupted HSI acquisition. This makes it flexible for different sensor configurations and particularly well-suited for practical remote sensing scenarios where large annotated hyperspectral datasets are limited. To address the ill-posed nature of unsupervised inpainting, we embed equivariant consistency constraints within the diffusion process. By leveraging the inherent geometric symmetries and intrinsic characteristics of HSIs, HyDiff-EI bridges the gap between generative diffusion modeling and self-consistent physical priors. We empirically show that coupling diffusion modeling with equivariant priors substantially enhances noise robustness and generalizability. Extensive experiments on real-world datasets including Chikusei, Botswana, and EMIT demonstrate that HyDiff-EI offers remarkable inpainting quality over existing self-supervised and diffusion-based algorithms in both noiseless and noisy cases.
发表机构
- University of Edinburgh（爱丁堡大学）
机构由 AI 辅助整理，请以论文原文为准。
