This paper introduces two very fast and competitive hyperspectral image (HSI) restoration algorithms: fast hyperspectral denoising (FastHyDe), a denoising algorithm able to cope with Gaussian and Poissonian noise, and fast hyperspectral inpainting (FastHyIn), an inpainting algorithm to restore HSIs where some observations from known pixels in some known bands are missing. FastHyDe and FastHyIn fully exploit extremely compact and sparse HSI representations linked with their low-rank and self-similarity characteristics. In a series of experiments with simulated and real data, the newly introduced FastHyDe and FastHyIn compete with the state-of-the-art methods, with much lower computational complexity.

翻译：本文介绍两种非常快速和有竞争力的超光谱图像(HSI)恢复算法:快速超光谱除尘(FastHyde),一种能够应对高山和波索南噪音的脱色算法,以及快速超光谱涂漆(FastHyIn),一种用于恢复高光谱图像的涂漆算法,因为一些已知带中已知像素的某些观测缺失。快速HyDe 和 FastHyIn充分利用了与低级别和自相似性特征相关的极紧凑和稀少的HSI表现。在一系列模拟和真实数据的实验中,新引入的快速Hyde和FastHyIn与最先进的方法竞争,其计算复杂性要低得多。