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

By Lina Zhuang, José M. Bioucas-Dias. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018

326 citations recorded by the source corpus.

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.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/f282db09517cad7b4cb19aa06ac826e5aca39aa6

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In Computational Mechanics

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization (2018, background): “The reason is that the spectral signatures of both HSIs live on low dimensional subspaces [40].”