Hyperspectral Image (HSI)s cover hundreds or thousands of narrow spectral bands, conveying a wealth of spatial and spectral information. However, due to the instrumental errors and the atmospheric changes, the HSI obtained in practice are often contaminated by noise and dead pixels(lines), resulting in missing information that may severely compromise the subsequent applications. We introduce here a novel HSI missing pixel prediction algorithm, called Low Rank and Sparsity Constraint Plug-and-Play (LRS-PnP). It is shown that LRS-PnP is able to predict missing pixels and bands even when all spectral bands of the image are missing. The proposed LRS-PnP algorithm is further extended to a self-supervised model by combining the LRS-PnP with the Deep Image Prior (DIP), called LRS-PnP-DIP. In a series of experiments with real data, It is shown that the LRS-PnP-DIP either achieves state-of-the-art inpainting performance compared to other learning-based methods, or outperforms them.

翻译：高光谱图像（HSI）覆盖数百或数千个狭窄光谱波段，蕴含着丰富的空间与光谱信息。然而，由于仪器误差及大气变化，实际获取的HSI常受到噪声和死像素（死线）污染，导致信息缺失，严重损害后续应用效果。本文提出一种新型HSI缺失像素预测算法，即低秩与稀疏约束即插即用（LRS-PnP）算法。研究表明，即使在图像所有光谱波段均缺失的情况下，LRS-PnP也能有效预测缺失像素与波段。进一步地，我们将LRS-PnP与深度图像先验（DIP）相结合，将其扩展为自监督模型，称为LRS-PnP-DIP。在基于真实数据的一系列实验中，LRS-PnP-DIP或达到与其他学习方法相当的最优修复性能，或展现出更优效果。