Results 11 to 20 of about 2,210,509 (307)
The Shannon Total Variation [PDF]
Journal of Mathematical Imaging and Vision, 2017 Discretization schemes commonly used for total variation regularization lead to images that are difficult to interpolate, which is a real issue for applications requiring subpixel accuracy and aliasing control. In the present work, we reconciliate total variation with Shannon interpolation and study a Fourier-based estimate that behaves much better in ...
Abergel, Rémy, Moisan, Lionel
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Total Variation on a Tree [PDF]
SIAM Journal on Imaging Sciences, 2016 accepted to SIAM Journal on Imaging Sciences (SIIMS)
Kolmogorov, Vladimir +2 more
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Structure Tensor Total Variation [PDF]
SIAM Journal on Imaging Sciences, 2015 Summary: We introduce a novel generic energy functional that we employ to solve inverse imaging problems within a variational framework. The proposed regularization family, termed as structure tensor total variation (STV), penalizes the eigenvalues of the structure tensor and is suitable for both grayscale and vector-valued images.
Stamatios Lefkimmiatis +3 more
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nhamilto/total-variation: Total variation Python codes and demonstration data [PDF]
, 2020 Identification of atmospheric conditions within a multivariable atmospheric data set is a necessary step in the validation of emerging and existing high-fidelity models used to simulate wind plant flows and operation. Atmospheric conditions relevant for
nhamilto
core +1 more source
Airborne Radar Super-Resolution Imaging Based on Fast Total Variation Method
Remote Sensing, 2021 Total variation (TV) is an effective super-resolution method to improve the azimuth resolution and preserve the contour information of the target in airborne radar imaging.
Qiping Zhang +4 more
doaj +1 more source
Total roto-translational variation [PDF]
Numerische Mathematik, 2019 We consider curvature depending variational models for image regularization, such as Euler's elastica. These models are known to provide strong priors for the continuity of edges and hence have important applications in shape-and image processing. We consider a lifted convex representation of these models in the roto-translation space: In this space ...
Chambolle, Antonin, Pock, Thomas
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Remote Sensing, 2018 Exploration of multiple priors on observed signals has been demonstrated to be one of the effective ways for recovering underlying signals. In this paper, a new spectral difference-induced total variation and low-rank approximation (termed SDTVLA) method
Le Sun +4 more
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IEEE Access, 2020 The fusion of hyperspectral and multispectral images is an effective way to obtain hyperspectral super-resolution images with high spatial resolution. A hyperspectral image is a datacube containing two spatial dimensions and a spectral dimension.
Fei Ma, Feixia Yang, Yanwei Wang
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Signals, 2023 In this paper, we propose robust image-smoothing methods based on ℓ0 gradient minimization with novel gradient constraints to effectively suppress pseudo-edges. Simultaneously minimizing the ℓ0 gradient, i.e., the number of nonzero gradients in an image,
Ryo Matsuoka, Masahiro Okuda
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On the total variation of the Jacobian
Journal of Functional Analysis, 2004 Let \(\Omega\) be an open subset of \({\mathbb R}^2\), and \(u=(u^1,u^2)\in L^\infty_{\text{ loc}}(\Omega;{\mathbb R}^2)\cap W^{1,p}(\Omega;{\mathbb R}^2)\) for some \(p>1\). In the paper, a comparison is carried out among the classical Jacobian determinant \(\det Du\) defined a.e. in \(\Omega\), the distributional Jacobian determinant \(\text{ Det} Du\
I. FONSECA, N. FUSCO, MARCELLINI, PAOLO
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