Results 181 to 190 of about 19,283,190 (228)
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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 Generalized Variation on a Tree
SIAM Journal on Imaging ScienceszbMATH Open Web Interface contents unavailable due to conflicting licenses.
Muhamed Kuric +2 more
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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.
Tony F. Chan, Chiu-Kwong Wong
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IEEE Transactions on Geoscience and Remote Sensing, 2018
Several bandwise total variation (TV) regularized low-rank (LR)-based models have been proposed to remove mixed noise in hyperspectral images (HSIs). These methods convert high-dimensional HSI data into 2-D data based on LR matrix factorization.
Haiyan Fan +4 more
semanticscholar +1 more source
Several bandwise total variation (TV) regularized low-rank (LR)-based models have been proposed to remove mixed noise in hyperspectral images (HSIs). These methods convert high-dimensional HSI data into 2-D data based on LR matrix factorization.
Haiyan Fan +4 more
semanticscholar +1 more source
IEEE Transactions on Geoscience and Remote Sensing, 2018
Hyperspectral unmixing is an important processing step for many hyperspectral applications, mainly including: 1) estimation of pure spectral signatures (endmembers) and 2) estimation of the abundance of each endmember in each pixel of the image.
Xin-Ru Feng +5 more
semanticscholar +1 more source
Hyperspectral unmixing is an important processing step for many hyperspectral applications, mainly including: 1) estimation of pure spectral signatures (endmembers) and 2) estimation of the abundance of each endmember in each pixel of the image.
Xin-Ru Feng +5 more
semanticscholar +1 more source
Multiscale Modeling & Simulation, 2007
In this paper we analyze iterative regularization with the Bregman distance of the total variation seminorm. Moreover, we prove existence of a solution of the corresponding flow equation as introduced in [M. Burger, G. Gilboa, S. Osher, and J. Xu, Commun. Math. Sci., 4 (2006), pp.
Martin Burger 0001 +3 more
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In this paper we analyze iterative regularization with the Bregman distance of the total variation seminorm. Moreover, we prove existence of a solution of the corresponding flow equation as introduced in [M. Burger, G. Gilboa, S. Osher, and J. Xu, Commun. Math. Sci., 4 (2006), pp.
Martin Burger 0001 +3 more
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A Total Variation Model for Retinex
SIAM Journal on Imaging Sciences, 2011Human vision has the ability to recognize color under varying illumination conditions. Retinex theory is introduced to explain how the human visual system perceives color. The main aim of this paper is to present a total variation model for Retinex.
Michael K. Ng 0001, Wei Wang 0132
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Research on image inpainting algorithm of improved total variation minimization method
Journal of Ambient Intelligence and Humanized Computing, 2021Yuantao Chen +7 more
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Total Nuclear Variation and Jacobian Extensions of Total Variation for Vector Fields
IEEE Transactions on Image Processing, 2014We explore a class of vectorial total variation (VTV) measures formed as the spatial sum of a pixel-wise matrix norm of the Jacobian of a vector field. We give a theoretical treatment that indicates that, while color smearing and affine-coupling bias (often reported as gray-scale bias) are typically cited as drawbacks for VTV, these are actually ...
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Iterative Methods for Total Variation Denoising
SIAM Journal on Scientific Computing, 1996The paper is concerned with computing the minimization of the total variation (TV)-penalized least squares functional. A fixed point algorithm is presented and compared with other minimization schemes. This is an alternative approach to minimizing the functional considered in the paper, called ``lagged diffusivity fixed point iteration'' and denoted by
Curtis R. Vogel, Mary E. Oman
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