用自监督方法修复高光谱图像缺失数据，效果优于现有技术。
Self-supervised Deep Hyperspectral Inpainting with the Plug and Play and Deep Image Prior Models
- 结合低秩稀疏模型与深度先验，利用数据内在结构修复图像
- 在多个数据集上实现最佳视觉与定量指标表现
- 算法稳定可收敛，适合真实场景的高光谱图像恢复
高光谱图像由数百个窄而连续的光谱波段组成，包含场景物质组成的丰富信息。但这些图像常受噪声、失真或数据丢失影响，显著降低质量与可用性。本文提出一种收敛性保障的算法 LRS-PnP-DIP(1-Lip)，有效解决了此前 DHP 方法存在的不稳定性问题。该算法将成功的低秩与稀疏联合模型拓展至更广泛的数据结构挖掘，超越传统受限的子空间联合模型。稳定性分析证明其在温和假设下可保证收敛，这对实际应用至关重要。大量实验表明，所提方法在视觉和量化指标上均持续优于现有方法，达到当前最优性能。
原文摘要 · Abstract (English)
Hyperspectral images are typically composed of hundreds of narrow and contiguous spectral bands, each containing information regarding the material composition of the imaged scene. However, these images can be affected by various sources of noise, distortions, or data loss, which can significantly degrade their quality and usefulness. This paper introduces a convergent guaranteed algorithm, LRS-PnP-DIP(1-Lip), which successfully addresses the instability issue of DHP that has been reported before. The proposed algorithm extends the successful joint low-rank and sparse model to further exploit the underlying data structures beyond the conventional and sometimes restrictive unions of subspace models. A stability analysis guarantees the convergence of the proposed algorithm under mild assumptions , which is crucial for its application in real-world scenarios. Extensive experiments demonstrate that the proposed solution consistently delivers visually and quantitatively superior inpainting results, establishing state-of-the-art performance.
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