Hyperspectral Image Denoising Employing a Spectral–Spatial Adaptive Total Variation Model

By Qiangqiang Yuan, Liangpei Zhang, Huanfeng Shen. IEEE Transactions on Geoscience and Remote Sensing, 2012

587 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/b0d7f39543b3e0372d8d15377dbc19246b0412cb

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In Computer Vision and Pattern Recognition

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Citations in the corpus

Estimating Ground‐Level PM2.5 by Fusing Satellite and Station Observations: A Geo‐Intelligent Deep Learning Approach (2017, background): “The nearer observations are more informative than further ones (Tobler, 1970; Yuan et al., 2012).”
Graph-Regularized Low-Rank Representation for Destriping of Hyperspectral Images (2013, methodology): “2–5 show the destriping results of the four subimages with the moment-matching method [9], the TV-based denoising method [39], the Ksvd method [40], and the proposed method, respectively.”
Hyperspectral Image Restoration Using Low-Rank Matrix Recovery (2014, methodology): “The value of each pixel in the HSI is then determined by taking the average of the restored patches at this pixel, which will suppress the possible artifacts in the neighborhood of the boundaries of patches.”
Noise Reduction in Hyperspectral Imagery: Overview and Application (2018, methodology): “Spectral and spectral-spatial penalty-based approaches [38,39,42,44] have been developed to incorporate spectral information.”
Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition (2017, methodology): “In [9], an efficient HSI denoising procedure is proposed by designing a spectral–spatial adaptive total variation (TV) model, where the spectral noise differences and spatial information differences are both considered.”
Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration (2016, methodology): “It describes an illposed inverse problem and has been widely used in image processing [19].”
Image super-resolution: The techniques, applications, and future (2016, background): “, digital color images or hyperspectral images), where the emphasis has been on the preservation of spectral information, while enhancing the spatial resolution [118,119].”
Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations (2018, methodology): “These characteristics have been exploited, namely, in low-rank matrix approximation [17]–[21], in the structured tensor TV-based regularization [22], and in the adaptive spectrum-weighted sparse Bayesian dictionary learning method (ABPFA) [23].”
Minimum Noise Fraction versus Principal Component Analysis as a Preprocessing Step for Hyperspectral Imagery Denoising (2016, methodology): “Yuan et al. (2012) worked on hyperspectral image denoising by employing a spectral-spatial adaptive total variation model.”
A Convex Formulation for Hyperspectral Image Superresolution via Subspace-Based Regularization (2014, background): “Second, matrix inversion, usually a costly operation, is easily performed, again through the use of FFTs, under certain conditions that are met in this case.”
A Multiscale and Multidepth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening (2017, background): “tral image denoising [51], [52], aerial scene classification [53], [54], and target detection [55].”
Hyperspectral Remote Sensing Data Analysis and Future Challenges (2013, methodology): “, employing spatial-spectral information [17] or employing tensor decompositions and multilinear algebra [18].”
Missing Information Reconstruction of Remote Sensing Data: A Technical Review (2015, background): “, the curvature-driven diffusion (CDD) models [9], high-order TV [30], and spectral-spatial adaptive TV [31].”
Hyperspectral image noise reduction based on rank-1 tensor decomposition (2013, methodology): “To verify the effectiveness of the proposed algorithm, the proposed R1TD tensor model is compared with several competitive methods: the channel-by-channel locally adaptive Wiener filter (CCWF) (Banham and Katsaggelos, 1997), channel-by-channel SVD (CCSVD) (Andrews and Patterson, 1976), SSAHTV (Yuan ”