Combinatorics

Physics

Pattern recognition (psychology)

Noise reduction

Factorization

Singular value decomposition

Subspace topology

Mathematics

Noise (video)

Algorithm

Image (mathematics)

Impulse noise

Hyperspectral imaging

Pixel

Rank (graph theory)

Artificial intelligence

Eigenvalues and eigenvectors

Computer science

Quantum mechanics

Gaussian noise

Matrix decomposition

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Hyperspectral images (HSIs) are unavoidably contaminated by different types of noise during data acquisition and transmission, e.g., Gaussian noise, impulse noise, stripes, and deadlines. A variety of mixed noise reduction approaches are developed for HSI, in which the subspace-based methods have achieved comparable performance. In this paper, a novel subspace-based nonlocal low-rank and sparse factorization (SNLRSF) method is proposed to remove the mixture of several types of noise. The SNLRSF method explores spectral low rank based on the fact that spectral signatures of pixels lie in a low-dimensional subspace and employs the nonlocal low-rank factorization to take the spatial nonlocal self-similarity into consideration. At the same time, the successive singular value decomposition (SVD) low-rank factorization algorithm is used to estimate three-dimensional (3-D) tensor generated by nonlocal similar 3-D patches. Moreover, the well-known augmented Lagrangian method is adopted to solve final denoising model efficiently. The experimental results over simulated and real datasets demonstrate that the proposed approach outperforms the related state-of-the-art methods in terms of visual quality and quantitative evaluation.

Hyperspectral Image Denoising via Subspace-Based Nonlocal Low-Rank and Sparse Factorization