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Hyperspectral Image (HSI)s cover hundreds or thousands of narrow spectralbands, conveying a wealth of spatial and spectral information. However, due tothe instrumental errors and the atmospheric changes, the HSI obtained inpractice are often contaminated by noise and dead pixels(lines), resulting inmissing information that may severely compromise the subsequent applications.We introduce here a novel HSI missing pixel prediction algorithm, called LowRank and Sparsity Constraint Plug-and-Play (LRS-PnP). It is shown that LRS-PnPis able to predict missing pixels and bands even when all spectral bands of theimage are missing. The proposed LRS-PnP algorithm is further extended to aself-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 thatthe LRS-PnP-DIP either achieves state-of-the-art inpainting performancecompared to other learning-based methods, or outperforms them.

Self-Supervised Hyperspectral Inpainting with the Optimisation inspired Deep Neural Network Prior