Problem
Research questions and friction points this paper is trying to address.
Hyperspectral Image
Inpainting
Self-supervised
Equivariant
Hyperspectral Diffusion Equivariant Imaging (HyDiff-EI): A Self-supervised Framework for Hyperspectral Image Inpainting
🤖 AI Summary
提出了一种名为HyDiff-EI的自监督框架，通过嵌入等变一致性约束于扩散过程中，解决了高光谱图像修复问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hyperspectral Diffusion
Equivariant Imaging
Self-supervised Framework
Test-time Optimization
Equivariant Priors
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