Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations

TL;DR

This paper presents two rapid hyperspectral image restoration algorithms, FastHyDe for denoising and FastHyIn for inpainting, which leverage low-rank and sparse representations to achieve competitive performance with lower computational costs.

Contribution

Introduction of two fast hyperspectral image restoration algorithms that exploit low-rank and sparse representations for improved efficiency and effectiveness.

Findings

FastHyDe effectively handles Gaussian and Poisson noise.

FastHyIn accurately restores missing data in hyperspectral images.

Both methods outperform state-of-the-art techniques in speed and accuracy.

Abstract

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.