  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.
