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Showing 1 to 20 of 117 for “"total variation"”.

  1. Unrolling of Graph Total Variation for Image Denoising

    … optimization problem for denoising using a graph total variation prior is formulated. Its solution is interpreted in an iterative procedure as a graph low-pass filter with an analytical frequency response. For fast implementation, this response is realized by Lanczos approximation. This method …

    york Repository record for Unrolling of Graph Total Variation for Image Denoising (opens in a new tab)

  2. Selection of Step Size for Total Variation Minimization in CT

    <p>Medical image reconstruction by total variation minimization is a newly developed area in computed tomography (CT). In compressed sensing literature, it hasbeen shown that signals with sparse representations in an orthonormal basis may be reconstructed via l1-minimization. Furthermore, if an …

    gsu Repository record for Selection of Step Size for Total Variation Minimization in CT (opens in a new tab)

  3. Numerical Simulation of 4th Order Total Variation Flow Problem by using C^0 IPDG Method

    … solution of the regularized fourth order total variation flow problem in material science representing surface relaxation below the roughening temperature. Based on regularization and a scaling in time and space, the problem is discretized implicitly in time by the backward Euler scheme …

    houston Repository record for Numerical Simulation of 4th Order Total Variation Flow Problem by using C^0 IPDG Method (opens in a new tab)

  4. Two-phase two-fluid model solver based on a high-resolution total variation diminishing scheme

    … developed using an innovative high-resolution, Total Variation Diminishing (TVD) scheme. The new scheme is derived first for scalar hyperbolic problems using the method of flux limiters, then extended to the two-phase two-fluid model. A hybridization of the monotone 1st-order upwind scheme and …

    uiuc Repository record for Two-phase two-fluid model solver based on a high-resolution total variation diminishing scheme (opens in a new tab)

  5. Higher Order Asymptotics for the MSE of Robust M-Estimators of Location on Shrinking Total Variation Neighborhoods

    … over to finite sample size in the context of total variation neighbourhoods. In contrast to usual higher order asymptotics, instead of giving approximations to distribution functions (or densities), we expand the risk directly by application of Edgeworth and Taylor expansions. In the context …

    bayreuth Repository record for Higher Order Asymptotics for the MSE of Robust M-Estimators of Location on Shrinking Total Variation Neighborhoods (opens in a new tab)

  6. Deep Learning Approaches for PDE-based Image Analysis and Beyond: From the Total Variation Flow to Medieval Paper Analysis

    … attention in the last few years is the total variation (TV) flow. The TV flow generates a scale-space representation of an image based on the TV functional. This gradient flow has desirable features for images such as sharp edges and enables spectral, scale, and texture analysis. Based …

    cambridge Repository record for Deep Learning Approaches for PDE-based Image Analysis and Beyond: From the Total Variation Flow to Medieval Paper Analysis (opens in a new tab)

  7. Topology Optimization Using Load Path and Homogenization

    … space, there are two load paths with their total derivative equal to the transferred load, their partial derivatives related to stress tensor, and satisfying equilibrium. In the presence of a body load the stress tensor can be decomposed into solenoidal and irrotational fields using Gurtin …

    embry-riddle Repository record for Topology Optimization Using Load Path and Homogenization (opens in a new tab)

  8. First-order gradient regularisation methods for image restoration: reconstruction of tomographic images with thin structures and denoising piecewise affine images

    The focus of this thesis is variational image restoration techniques that involve novel non-smooth first-order gradient regularisers: Total Variation (TV) regularisation in image and data space for reconstruction of thin structures from PET data and regularisers given by an infimal-convolution of …

    cambridge Repository record for First-order gradient regularisation methods for image restoration: reconstruction of tomographic images with thin structures and denoising piecewise affine images (opens in a new tab)

  9. A Continuum Thermomechanical Model for Energetic Materials

    … using essentially non-oscillatory and total variation diminishing methods. The solutions reveal extremely rich behavior, including complex wave phenomena, strain localization phenomena, and changes of material phase.

    uiuc Repository record for A Continuum Thermomechanical Model for Energetic Materials (opens in a new tab)

  10. Development and implementation of efficient noise suppression methods for emission computed tomography

    … objective function with the non-differentiable total variation (TV) regularizer. The algorithm was modified to optimize the proposed reconstruction objective functions. </p> <p> First, two novel TV-based regularizers—high-order total variation (HOTV) and infimal convolution total variation

    syracuse-diss Repository record for Development and implementation of efficient noise suppression methods for emission computed tomography (opens in a new tab)

  11. Hessian Matrix-Free Lagrange-Newton-Krylov-Schur-Schwarz Methods for Elliptic Inverse Problems

    … community. In this study, iterative Tikhonov and Total Variation regularizations are proposed and compared to the standard regularizations and each other. For exact data with jump discontinuities the standard and iterative Total Variation regulations are superior to the standard and iterative …

    odu Repository record for Hessian Matrix-Free Lagrange-Newton-Krylov-Schur-Schwarz Methods for Elliptic Inverse Problems (opens in a new tab)

  12. Information contraction and decomposition

    … (KL) divergence, X²-divergence, and total variation (TV) distance, through channels (or the contraction of mutual f-information along Markov chains) is quantitatively captured by the well-known data processing inequalities.

    mit Repository record for Information contraction and decomposition (opens in a new tab)

  13. Inheritance and Components of Resistance to Bipolaris Maydis Race O (Maize, Corn, Breeding)

    … families, accounting for 49.0% to 96.9% of the total variation. Significant dominance effects were detected in all but one family but accounted for only 2.5% to 46.9% of the total variation. Estimates of broad sense heritabilities ranged from 29.5% to 69.1% and the estimated numbers of effective …

    uiuc Repository record for Inheritance and Components of Resistance to Bipolaris Maydis Race O (Maize, Corn, Breeding) (opens in a new tab)

  14. Novel higher order regularisation methods for image reconstruction

    In this thesis we study novel higher order total variation-based variational methods for digital image reconstruction. These methods are formulated in the context of Tikhonov regularisation. We focus on regularisation techniques in which the regulariser incorporates second order derivatives or a …

    cambridge Repository record for Novel higher order regularisation methods for image reconstruction (opens in a new tab)

  15. Mathematical Challenges in Electron Microscopy

    … sparsity promoting functionals such as the total variation and its extensions have been shown to be very powerful for recovering accurate physical quantities from very little and/or poor quality data. While sparsity-promoting reconstruction methods are powerful, they can also be slow, …

    cambridge Repository record for Mathematical Challenges in Electron Microscopy (opens in a new tab)

  16. Comparing Distributions: Invariance Principles & Mismatched Guesswork

    … from the Shannon entropy. Given a bound on the total variation distance between two finite distributions, we give a bound on the difference in guesswork between those distributions and study the geometrical properties of the problem in the non-asymptotic setting.

    mit Repository record for Comparing Distributions: Invariance Principles & Mismatched Guesswork (opens in a new tab)

  17. Image processing in echography and MRI

    … problems, implementing<br/>restoration with a total variation first order Lagrangian method, and abstraction with<br/>clustering and local linear expansion.

    soton Repository record for Image processing in echography and MRI (opens in a new tab)

  18. Sparse Learning of Nonlinear PDE Dynamics using Kalman Smoothing

    … relied on methods such as finite difference, L1 total variation minimization, or Savitzky-Golay filtering. Kalman smoothing, a classical approach for data assimilation with known noise statistics, has recently been incorporated into pysindy alongside hyperparameter optimization to enhance data …

    washington Repository record for Sparse Learning of Nonlinear PDE Dynamics using Kalman Smoothing (opens in a new tab)

  19. Determination of cardiac output across a range of values in horses by M-mode echocardiography and thermodilution

    … variability accounted for 28% of the total variation in COTD values and 64% of the total variation in COecho values. Results might be improved if the mean of 3-5 consecutive beats was used for each measurement, but as determined in this experiment, COecho is too variable to have …

    vt Repository record for Determination of cardiac output across a range of values in horses by M-mode echocardiography and thermodilution (opens in a new tab)

  20. The inverse medium problem for Timoshenko beams and frames : damage detection and profile reconstruction in the time-domain

    … multiplicity by means of either Tikhonov or Total Variation regularization. We report on numerical experiments using synthetic data that show satisfactory reconstruction of a variety of profiles, including smoothly and sharply varying profiles, as well as profiles exhibiting localized …

    texas Repository record for The inverse medium problem for Timoshenko beams and frames : damage detection and profile reconstruction in the time-domain (opens in a new tab)

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