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Color Image Restoration by Saturation-Value Total Variation

SIAM Journal of Imaging Sciences, 2019
Color image restoration is one of the important tasks in color image processing. Total variation regularizaton was proposed and employed for the recovery of edges in a grayscale image.
Zhigang Jia, M. Ng, Wei Wang
semanticscholar   +1 more source

Total Cyclic Variation and Generalizations

Journal of Mathematical Imaging and Vision, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Cremers, Daniel, Strekalovskiy, Evgeny
openaire   +2 more sources

Noise-Robust Hyperspectral Image Classification via Multi-Scale Total Variation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019
In this paper, a novel multi-scale total variation method is proposed to extract structural features from hyperspectral images (HSIs), which consists of the following steps.
Puhong Duan   +3 more
semanticscholar   +1 more source

Image Completion Using Low Tensor Tree Rank and Total Variation Minimization

IEEE transactions on multimedia, 2019
Tensor completion recovers missing entries of multiway data. Most of the current methods exploit the low-rank tensor structure for image completion applications. In this paper, we simultaneously exploit the globally multidimensional structure and locally
Yipeng Liu, Zhen Long, Ce Zhu
semanticscholar   +1 more source

Total Variation in Imaging

2011
The use of total variation as a regularization term in imaging problems was motivated by its ability to recover the image discontinuities. This is at the basis of his numerous applications to denoising, optical flow, stereo imaging and 3D surface reconstruction, segmentation, or interpolation, to mention some of them.
CASELLES V, CHAMBOLLE A, NOVAGA, MATTEO
openaire   +3 more sources

Spatial–Spectral Total Variation Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising

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

Hyperspectral Unmixing Using Sparsity-Constrained Deep Nonnegative Matrix Factorization With Total Variation

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

Outer Measures and Total Variation

Canadian Mathematical Bulletin, 1981
In this note we collect some observations on the outer measures ψf and ψf that have been introduced in [4] and which describe the total variation of the function f. These properties have direct applications to the study of the derivative and the relative derivative. For definitions and notation the reader is referred to [4].
openaire   +1 more source

Inverse Total Variation Flow

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.
M. Burger   +3 more
openaire   +1 more source

Research on image inpainting algorithm of improved total variation minimization method

Journal of Ambient Intelligence and Humanized Computing, 2021
Yuantao Chen   +7 more
semanticscholar   +1 more source

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