Denoising and Dimensionality Reduction Using Multilinear Tools for Hyperspectral Images

By Nadine Renard, Salah Bourennane, Jacques Blanc-Talon. IEEE Geoscience and Remote Sensing Letters, 2008

312 citations recorded by the source corpus.

Subfields: Computational Mathematics, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/eaa9acd30a84fcf64a24bbe317031250ce9faf7d

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Citations in the corpus

Hyperspectral Image Denoising via Tensor Low-Rank Prior and Unsupervised Deep Spatial–Spectral Prior (2022, methodology): “Based on the Tucker decomposition [29], Renard et al. [30] proposed a multilinear algebra method, which processes spatial and spectral dimensions jointly.”
Hyperspectral image noise reduction based on rank-1 tensor decomposition (2013, methodology): “Examples of such approaches include multidimensional filtering based on Tucker tensor decomposition (Bourennane et al., 2011; Muti and Bourennane, 2005; Renard and Bourennane, 2008; Renard et al., 2008), and the use of the kernel trick in Tucker decomposition (Karami et al., 2011).”
Kronecker-Basis-Representation Based Tensor Sparsity and Its Applications to Tensor Recovery (2018, unknown): “…state-of-the-arts for 2D extended band-wise meth-ods; 3D-cube K-SVD [18], 5 ANLM3D [46] 6 and BM4D [44], [45], 7 representing state-of-the-arts for 2D extended 3D-cube-based methods; LRTA [49], PARAFAC [41], 8 TDL [52], 9 t-SVD [74] 10 (using the same denoising framework as shown in Fig.”
Noise Reduction in Hyperspectral Imagery: Overview and Application (2018, methodology): “In [47], a similar idea was exploited by applying a higher spectral reduction.”
Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image Denoising (2014, methodology): “…including peak signal-to-noise ratio (PSNR), structure similarity (SSIM [26]), feature similarity (FSIM [30]), erreur relative globale adimension-nelle de synth ` e se (ERGAS [25]) and spectral angle map-2 (f) ANLM3D [18] (g) BM4D [16] (h) LRTA [23] (i) PARAFAC [14] (j) Ours per (SAM [29]).”
A tutorial on task-parameterized movement learning and retrieval (2015, background): “…data oﬀers a rich set of data decomposition techniques, whose advantage has been demonstrated in computer imaging ﬁelds such as face processing [98], video analysis [107], geoscience [75] or neuroimaging [8], but which remains an uncharted territory in robotics and motor skills learning.”