Results 31 to 40 of about 19,283,190 (228)
A new regularization term based on second order total generalized variation for image denoising problems [PDF]
Iranian Journal of Numerical Analysis and Optimization, 2019 Variational models are one of the most efficient techniques for image denoising problems. A variational method refers to the technique of optimizing a functional in order to restore appropriate solutions from observed data that best fit the original ...
E. Tavakkol +2 more
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Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017 Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise during the acquisition process, e.g., Gaussian noise, impulse noise, dead lines, stripes, etc.
Yao Wang +5 more
semanticscholar +1 more source
Total Variation Isoperimetric Profiles [PDF]
SIAM Journal on Applied Algebra and Geometry, 2019 Applications such as political redistricting demand quantitative measures of geometric compactness to distinguish between simple and contorted shapes. While the isoperimetric quotient, or ratio of area to perimeter squared, is commonly used in practice, it is sensitive to noisy data and irrelevant geographic features like coastline.
Daryl R. DeFord +3 more
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Stable image reconstruction using total variation minimization [PDF]
, 2013 This article presents near-optimal guarantees for accurate and robust image recovery from under-sampled noisy measurements using total variation minimization.
Needell, Deanna, Ward, Rachel
core +4 more sources
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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A Variational Model for Sea Image Enhancement
Remote Sensing, 2018 The purpose of sea image enhancement is to enhance the information of the waves, whose contrast is generally weak. Enhancement effect is often affected by impulse-type noise and non-uniform illumination.
Mingzhu Song +4 more
doaj +1 more source
Seismic Data Denoising Based on Sparse and Low-Rank Regularization
Energies, 2020 Seismic denoising is a core task of seismic data processing. The quality of a denoising result directly affects data analysis, inversion, imaging and other applications.
Shu Li +4 more
doaj +1 more source
Total Variation Wavelet Thresholding [PDF]
Journal of Scientific Computing, 2007 The authors consider the noise removal and reducing edge artifacts generated by wavelet thresholdings in image denoising and compression. It is known that wavelet thresholdings may generate oscillations near discontinuities. Since about 1990, partial differential equations (PDE) models have been used in image processing in the pixel domain [see, e.g., \
Tony F. Chan, Haomin Zhou 0001
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Poisson image denoising based on fractional-order total variation
Inverse Problems and Imaging, 2020 Poisson noise is an important type of electronic noise that is present in a variety of photon-limited imaging systems. Different from the Gaussian noise, Poisson noise depends on the image intensity, which makes image restoration very challenging ...
M. R. Chowdhury +3 more
semanticscholar +1 more source
Compressive Hyperspectral Imaging Using Progressive Total Variation [PDF]
, 2014 Compressed Sensing (CS) is suitable for remote acquisition of hyperspectral images for earth observation, since it could exploit the strong spatial and spectral correlations, llowing to simplify the architecture of the onboard sensors. Solutions proposed
Barducci, Alessandro +4 more
core +2 more sources