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Graph and Total Variation Regularized Low-Rank Representation for Hyperspectral Anomaly Detection
IEEE Transactions on Geoscience and Remote Sensing, 2020Anomaly detection is of great importance among hyperspectral applications, which aims at locating targets that are spectrally different from their surrounding background. A variety of anomaly detection methods have been proposed in the past.
Tongkai Cheng, Bin Wang
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IEEE Transactions on Geoscience and Remote Sensing, 2020
Hyperspectral images (HSIs) are normally corrupted by a mixture of various noise types, which degrades the quality of the acquired image and limits the subsequent application.
Hongyan Zhang +3 more
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Hyperspectral images (HSIs) are normally corrupted by a mixture of various noise types, which degrades the quality of the acquired image and limits the subsequent application.
Hongyan Zhang +3 more
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Anisotropic Total Variation Filtering
Applied Mathematics & Optimization, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Grasmair, Markus, Lenzen, Frank
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Total variation blind deconvolution
IEEE Transactions on Image Processing, 1998In this paper, we present a blind deconvolution algorithm based on the total variational (TV) minimization method proposed. The motivation for regularizing with the TV norm is that it is extremely effective for recovering edges of images as well as some blurring functions, e.g., motion blur and out-of-focus blur.
Chan, Tony F., Wong, Chiukwong
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Total Variation Wavelet Inpainting
Journal of Mathematical Imaging and Vision, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chan, Tony F. +2 more
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Remote Sensing Image Reconstruction Using Tensor Ring Completion and Total Variation
IEEE Transactions on Geoscience and Remote Sensing, 2019Time-series remote sensing (RS) images are often corrupted by various types of missing information such as dead pixels, clouds, and cloud shadows that significantly influence the subsequent applications.
Wei He +3 more
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Hyperspectral Unmixing via Total Variation Regularized Nonnegative Tensor Factorization
IEEE Transactions on Geoscience and Remote Sensing, 2019Hyperspectral unmixing decomposes a hyperspectral imagery (HSI) into a number of constituent materials and associated proportions. Recently, nonnegative tensor factorization (NTF)-based methods have been proposed for hyperspectral unmixing thanks to ...
Fengchao Xiong +3 more
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SIAM Journal on Imaging Sciences, 2010
The novel concept of total generalized variation of a function $u$ is introduced, and some of its essential properties are proved. Differently from the bounded variation seminorm, the new concept involves higher-order derivatives of $u$. Numerical examples illustrate the high quality of this functional as a regularization term for mathematical imaging ...
Kristian Bredies +2 more
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The novel concept of total generalized variation of a function $u$ is introduced, and some of its essential properties are proved. Differently from the bounded variation seminorm, the new concept involves higher-order derivatives of $u$. Numerical examples illustrate the high quality of this functional as a regularization term for mathematical imaging ...
Kristian Bredies +2 more
openaire +1 more source