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The Total Variation Flow∗
THE TOTAL VARIATION FLOW∗ Fuensanta Andreu and Jos´eM. Maz´on† Abstract We summarize in this lectures some of our results about the Min- imizing Total Variation Flow, which have been mainly motivated by problems arising in Image Processing. First, we recall the role played by the Total Variation in Image Processing, in particular the varia- tional formulation of the restoration problem. Next we outline some of the tools we need: functions of bounded variation (Section 2), par- ing between measures and bounded functions (Section 3) and gradient flows in Hilbert spaces (Section 4). Section 5 is devoted to the Neu- mann problem for the Total variation Flow. Finally, in Section 6 we study the Cauchy problem for the Total Variation Flow. 1 The Total Variation Flow in Image Pro- cessing We suppose that our image (or data) ud is a function defined on a bounded and piecewise smooth open set D of IRN - typically a rectangle in IR2. Gen- erally, the degradation of the image occurs during image acquisition and can be modeled by a linear and translation invariant blur and additive noise. The equation relating u, the real image, to ud can be written as ud = Ku + n, (1) where K is a convolution operator with impulse response k, i.e., Ku = k ∗ u, and n is an additive white noise of standard deviation σ. In practice, the noise can be considered as Gaussian. ∗Notes from the course given in the Summer School “Calculus of Variations”, Roma, July 4-8, 2005 †Departamento de An´alisis Matem´atico, Universitat de Valencia, 46100 Burjassot (Va- lencia), Spain, [email protected], [email protected] 1 The problem of recovering u from ud is ill-posed. -
An Approach to the Willmore Conjecture ∗
An approach to the Willmore conjecture ∗ Peter Topping Abstract We highlight one of the methods developed in [6] to give partial answers to the Willmore conjecture, and discuss how it might be used to prove the complete conjecture. The Willmore conjecture, dating from around 1965, asserts that the integral of the square of the mean curvature H over a torus immersed in R3 (with the induced metric) should always be at least 2π2: 2 2 W := Z H 2π : T 2 ≥ A weaker lower bound of 4π for this `Willmore energy' is rather easy to obtain (see [6]) but this bound is known not to be sharp (see [5]) unless we change the problem to allow integrals over spheres instead of tori. It is well known that this problem has an equivalent formulation in S3. Indeed, if we transport the torus from R3 to S3 via inverse stereographic projection, the Willmore energy may be written in terms of the new induced metric on the torus, and the new mean curvature, as 2 W = Z (1 + H ): T 2 This energy is preserved if we move the torus via conformal transformations of the ambient S3 (see [6]). Once formulated within S3, we see that the conjecture asserts that the Clifford torus, which has area 2π2 and satisfies H = 0, should be a minimiser for W . Of course, the Clifford torus is the case r = 1 of the family of flat tori p2 2 2 3 2 Tr := (z1; z2) C : z1 = r; z2 = 1 r , S , C f 2 j j j j p − g ! ! 3 which, as r varies within (0; 1), foliate S minus the great circles T0 and T1. -
Discrete Willmore Flow
Eurographics Symposium on Geometry Processing (2005) M. Desbrun, H. Pottmann (Editors) Discrete Willmore Flow Alexander I. Bobenko1 and Peter Schröder2 1TU Berlin 2Caltech Abstract The Willmore energy of a surface, R (H2 − K)dA, as a function of mean and Gaussian curvature, captures the deviation of a surface from (local) sphericity. As such this energy and its associated gradient flow play an impor- tant role in digital geometry processing, geometric modeling, and physical simulation. In this paper we consider a discrete Willmore energy and its flow. In contrast to traditional approaches it is not based on a finite ele- ment discretization, but rather on an ab initio discrete formulation which preserves the Möbius symmetries of the underlying continuous theory in the discrete setting. We derive the relevant gradient expressions including a linearization (approximation of the Hessian), which are required for non-linear numerical solvers. As examples we demonstrate the utility of our approach for surface restoration, n-sided hole filling, and non-shrinking surface smoothing. Categories and Subject Descriptors (according to ACM CCS): G.1.8 [Numerical Analysis]: Partial Differential Equa- tions; I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling; I.6.8 [Simulation and Model- ing]: Types of Simulation. Keywords: Geometric Flow; Discrete Differential Geometry; Willmore Energy; Variational Surface Modeling; Digital Geometry Processing. 1. Introduction by a surface energy of the form Z The Willmore energy of a surface S ⊂ 3 is given as 2 R E(S) = α + β(H − H0) − γK dA, S Z Z 2 2 EW (S) = (H − K)dA = 1/4 (κ1 − κ2) dA, the so-called Canham-Helfrich model [Can70, Hel73] S S (H0 denotes the “spontaneous” curvature where κ1 and κ2 denote the principal curvatures, H = which plays an important role in thin- 1/2(κ1 + κ2) and K = κ1κ2 the mean and Gaussian curva- shells [GKS02, BMF03, GHDS03]). -
Total Variation Deconvolution Using Split Bregman
Published in Image Processing On Line on 2012{07{30. Submitted on 2012{00{00, accepted on 2012{00{00. ISSN 2105{1232 c 2012 IPOL & the authors CC{BY{NC{SA This article is available online with supplementary materials, software, datasets and online demo at http://dx.doi.org/10.5201/ipol.2012.g-tvdc 2014/07/01 v0.5 IPOL article class Total Variation Deconvolution using Split Bregman Pascal Getreuer Yale University ([email protected]) Abstract Deblurring is the inverse problem of restoring an image that has been blurred and possibly corrupted with noise. Deconvolution refers to the case where the blur to be removed is linear and shift-invariant so it may be expressed as a convolution of the image with a point spread function. Convolution corresponds in the Fourier domain to multiplication, and deconvolution is essentially Fourier division. The challenge is that since the multipliers are often small for high frequencies, direct division is unstable and plagued by noise present in the input image. Effective deconvolution requires a balance between frequency recovery and noise suppression. Total variation (TV) regularization is a successful technique for achieving this balance in de- blurring problems. It was introduced to image denoising by Rudin, Osher, and Fatemi [4] and then applied to deconvolution by Rudin and Osher [5]. In this article, we discuss TV-regularized deconvolution with Gaussian noise and its efficient solution using the split Bregman algorithm of Goldstein and Osher [16]. We show a straightforward extension for Laplace or Poisson noise and develop empirical estimates for the optimal value of the regularization parameter λ. -
Guaranteed Deterministic Bounds on the Total Variation Distance Between Univariate Mixtures
Guaranteed Deterministic Bounds on the Total Variation Distance between Univariate Mixtures Frank Nielsen Ke Sun Sony Computer Science Laboratories, Inc. Data61 Japan Australia [email protected] [email protected] Abstract The total variation distance is a core statistical distance between probability measures that satisfies the metric axioms, with value always falling in [0; 1]. This distance plays a fundamental role in machine learning and signal processing: It is a member of the broader class of f-divergences, and it is related to the probability of error in Bayesian hypothesis testing. Since the total variation distance does not admit closed-form expressions for statistical mixtures (like Gaussian mixture models), one often has to rely in practice on costly numerical integrations or on fast Monte Carlo approximations that however do not guarantee deterministic lower and upper bounds. In this work, we consider two methods for bounding the total variation of univariate mixture models: The first method is based on the information monotonicity property of the total variation to design guaranteed nested deterministic lower bounds. The second method relies on computing the geometric lower and upper envelopes of weighted mixture components to derive deterministic bounds based on density ratio. We demonstrate the tightness of our bounds in a series of experiments on Gaussian, Gamma and Rayleigh mixture models. 1 Introduction 1.1 Total variation and f-divergences Let ( R; ) be a measurable space on the sample space equipped with the Borel σ-algebra [1], and P and QXbe ⊂ twoF probability measures with respective densitiesXp and q with respect to the Lebesgue measure µ. -
Discrete Willmore Flow
Eurographics Symposium on Geometry Processing (2005) M. Desbrun, H. Pottmann (Editors) Discrete Willmore Flow Alexander I. Bobenko1 and Peter Schröder2 1TU Berlin 2Caltech Abstract The Willmore energy of a surface, R (H2 − K)dA, as a function of mean and Gaussian curvature, captures the deviation of a surface from (local) sphericity. As such this energy and its associated gradient flow play an impor- tant role in digital geometry processing, geometric modeling, and physical simulation. In this paper we consider a discrete Willmore energy and its flow. In contrast to traditional approaches it is not based on a finite ele- ment discretization, but rather on an ab initio discrete formulation which preserves the Möbius symmetries of the underlying continuous theory in the discrete setting. We derive the relevant gradient expressions including a linearization (approximation of the Hessian), which are required for non-linear numerical solvers. As examples we demonstrate the utility of our approach for surface restoration, n-sided hole filling, and non-shrinking surface smoothing. Categories and Subject Descriptors (according to ACM CCS): G.1.8 [Numerical Analysis]: Partial Differential Equa- tions; I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling; I.6.8 [Simulation and Model- ing]: Types of Simulation. Keywords: Geometric Flow; Discrete Differential Geometry; Willmore Energy; Variational Surface Modeling; Digital Geometry Processing. 1. Introduction by a surface energy of the form Z The Willmore energy of a surface S ⊂ 3 is given as 2 R E(S) = α + β(H − H0) − γK dA, S Z Z 2 2 EW (S) = (H − K)dA = 1/4 (κ1 − κ2) dA, the so-called Canham-Helfrich model [Can70, Hel73] S S (H0 denotes the “spontaneous” curvature where κ1 and κ2 denote the principal curvatures, H = which plays an important role in thin- 1/2(κ1 + κ2) and K = κ1κ2 the mean and Gaussian curva- shells [GKS02, BMF03, GHDS03]). -
Calculus of Variations
MIT OpenCourseWare http://ocw.mit.edu 16.323 Principles of Optimal Control Spring 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 16.323 Lecture 5 Calculus of Variations • Calculus of Variations • Most books cover this material well, but Kirk Chapter 4 does a particularly nice job. • See here for online reference. x(t) x*+ αδx(1) x*- αδx(1) x* αδx(1) −αδx(1) t t0 tf Figure by MIT OpenCourseWare. Spr 2008 16.323 5–1 Calculus of Variations • Goal: Develop alternative approach to solve general optimization problems for continuous systems – variational calculus – Formal approach will provide new insights for constrained solutions, and a more direct path to the solution for other problems. • Main issue – General control problem, the cost is a function of functions x(t) and u(t). � tf min J = h(x(tf )) + g(x(t), u(t), t)) dt t0 subject to x˙ = f(x, u, t) x(t0), t0 given m(x(tf ), tf ) = 0 – Call J(x(t), u(t)) a functional. • Need to investigate how to find the optimal values of a functional. – For a function, we found the gradient, and set it to zero to find the stationary points, and then investigated the higher order derivatives to determine if it is a maximum or minimum. – Will investigate something similar for functionals. June 18, 2008 Spr 2008 16.323 5–2 • Maximum and Minimum of a Function – A function f(x) has a local minimum at x� if f(x) ≥ f(x �) for all admissible x in �x − x�� ≤ � – Minimum can occur at (i) stationary point, (ii) at a boundary, or (iii) a point of discontinuous derivative. -
Calculus of Variations in Image Processing
Calculus of variations in image processing Jean-François Aujol CMLA, ENS Cachan, CNRS, UniverSud, 61 Avenue du Président Wilson, F-94230 Cachan, FRANCE Email : [email protected] http://www.cmla.ens-cachan.fr/membres/aujol.html 18 september 2008 Note: This document is a working and uncomplete version, subject to errors and changes. Readers are invited to point out mistakes by email to the author. This document is intended as course notes for second year master students. The author encourage the reader to check the references given in this manuscript. In particular, it is recommended to look at [9] which is closely related to the subject of this course. Contents 1. Inverse problems in image processing 4 1.1 Introduction.................................... 4 1.2 Examples ....................................... 4 1.3 Ill-posedproblems............................... .... 6 1.4 Anillustrativeexample. ..... 8 1.5 Modelizationandestimator . ..... 10 1.5.1 Maximum Likelihood estimator . 10 1.5.2 MAPestimator ................................ 11 1.6 Energymethodandregularization. ....... 11 2. Mathematical tools and modelization 14 2.1 MinimizinginaBanachspace . 14 2.2 Banachspaces.................................... 14 2.2.1 Preliminaries ................................. 14 2.2.2 Topologies in Banach spaces . 17 2.2.3 Convexity and lower semicontinuity . ...... 19 2.2.4 Convexanalysis................................ 23 2.3 Subgradient of a convex function . ...... 23 2.4 Legendre-Fencheltransform: . ....... 24 2.5 The space of funtions with bounded variation . ......... 26 2.5.1 Introduction.................................. 26 2.5.2 Definition ................................... 27 2.5.3 Properties................................... 28 2.5.4 Decomposability of BV (Ω): ......................... 28 2.5.5 SBV ...................................... 30 2.5.6 Setsoffiniteperimeter . 32 2.5.7 Coarea formula and applications . -
Analysis Aspects of Willmore Surfaces
ANALYSIS ASPECTS OF WILLMORE SURFACES. Tristan Riviere` Departement Mathematik ETH Z¨urich (e–mail: [email protected]) (Homepage: http://www.math.ethz.ch/ riviere) 3 juillet 2007, Paris. Des EDP au calcul scientifique. En l’honneur de Luc Tartar. £ Curvatures for surfaces in ¢ . ¦ ¥ - ¤ oriented closed surface in . ¤ - § induced metric on . ¨ © - the unit normal to ¤ (Gauss map). ¡ Curvatures for surfaces in . 1 Curvatures for surfaces in is bilinear symmetric from The - - - ¨ § © ¤ 2nd Fundamental form induced metric on the unit normal to oriented closed surface in ¡ ¡ . ¢ Curvatures for surfaces in £ ¤ ¤ ¤ ¥ . ¨ ( ¦ ¥ Gauss map : ¤ ¦ ¤ ¨ © § ¥ ¦ into ¨ ¦ ¡ . ¢ £ ). © ¦ ¤ the normal direction to ¨ © ¦ ¡ ¢ ¢ £ £ . ¨ © ¥ ¦ ¤ . (1) 1 Curvatures for surfaces in is bilinear symmetric from The - - - ¨ § © ¤ 2nd Fundamental form induced metric on the unit normal to oriented closed surface in ¡ ¡ . ¢ Curvatures for surfaces in £ ¤ ¤ ¤ ¥ . ¨ ( ¦ ¥ Gauss map : ¨ ¤ ¦ § ¤ ¦ ¨ © § ¥ ¦ into ¨ ¦ ¡ . ¢ ¡ ¢ £ £ ). ¥ ¤ © ¦ ¤ £ ¥ the normal direction to § ¦ ¨ ¨ © © ¦ ¡ ¢ ¢ £ £ . ¨ © ¥ ¦ ¤ . (3) (2) 1 Curvatures for surfaces in is bilinear symmetric from The - - - - - - - ¨ Gauss curvature Mean curvature vector Mean curvature Principal curvatures § © ¤ 2nd Fundamental form induced metric on the unit normal to oriented closed surface in ¡ ¡ . ¢ : Curvatures for surfaces in £ : ¤ ¤ ¤ : ¢ ¥ . ¨ ( ¦ : ¥ Gauss map : ¨ ¤ £ ¦ § ¤ ¤ ¤ ¨ ¦ ¨ £ ¨ © and § ¤ £ ¥ ¦ £ ¤ into ¨ ¦ ¡ . ¡ . £ ¢ ¡ ¢ ¨ £ £ £ ). ¥ © ¤ © ¦ . © ¤ . £ ¥ the normal direction -
Range Convergence Monotonicity for Vector
RANGE CONVERGENCE MONOTONICITY FOR VECTOR MEASURES AND RANGE MONOTONICITY OF THE MASS JUSTIN DEKEYSER AND JEAN VAN SCHAFTINGEN Abstract. We prove that the range of sequence of vector measures converging widely satisfies a weak lower semicontinuity property, that the convergence of the range implies the strict convergence (convergence of the total variation) and that the strict convergence implies the range convergence for strictly convex norms. In dimension 2 and for Euclidean spaces of any dimensions, we prove that the total variation of a vector measure is monotone with respect to the range. Contents 1. Introduction 1 2. Total variation and range 4 3. Convergence of the total variation and of the range 14 4. Zonal representation of masses 19 5. Perimeter representation of masses 24 Acknowledgement 27 References 27 1. Introduction A vector measure µ maps some Σ–measurable subsets of a space X to vectors in a linear space V [1, Chapter 1; 8; 13]. Vector measures ap- pear naturally in calculus of variations and in geometric measure theory, as derivatives of functions of bounded variation (BV) [6] and as currents of finite mass [10]. In these contexts, it is natural to work with sequences of arXiv:1904.05684v1 [math.CA] 11 Apr 2019 finite Radon measures and to study their convergence. The wide convergence of a sequence (µn)n∈N of finite vector measures to some finite vector measure µ is defined by the condition that for every compactly supported continuous test function ϕ ∈ Cc(X, R), one has lim ϕ dµn = ϕ dµ. n→∞ ˆX ˆX 2010 Mathematics Subject Classification. -
Removability of Point Singularities of Willmore Surfaces
Annals of Mathematics, 160 (2004), 315–357 Removability of point singularities of Willmore surfaces By Ernst Kuwert and Reiner Schatzle¨ * Abstract We investigate point singularities of Willmore surfaces, which for example appear as blowups of the Willmore flow near singularities, and prove that closed Willmore surfaces with one unit density point singularity are smooth in codimension one. As applications we get in codimension one that the Willmore flow of spheres with energy less than 8π exists for all time and converges to a round sphere and further that the set of Willmore tori with energy less than 8π − δ is compact up to M¨obius transformations. 1. Introduction For an immersed closed surface f :Σ→ Rn the Willmore functional is defined by 1 W(f)= |H|2 dµ , 4 g Σ ∗ where H denotes the mean curvature vector of f, g = f geuc the pull-back metric and µg the induced area measure on Σ. The Gauss equations and the Gauss-Bonnet Theorem give rise to equivalent expressions 1 1 ◦ W(f)= |A|2 dµ + πχ(Σ) = |A |2 dµ +2πχ(Σ), 4 g 2 g Σ Σ ◦ − 1 ⊗ where A denotes the second fundamental form, A = A 2 g H its trace-free part and χ the Euler characteristic. The Willmore functional is scale invariant and moreover invariant under the full M¨obius group of Rn. Critical points of W are called Willmore surfaces or more precisely Willmore immersions. We always have W(f) ≥ 4π with equality only for round spheres; see [Wil] in codimension one, that is n = 3. -
Total Variation As a Local Filter∗
SIAM J. IMAGING SCIENCES c 2011 Society for Industrial and Applied Mathematics Vol. 4, No. 2, pp. 651–694 Total Variation as a Local Filter∗ † ‡ C´ecile Louchet and Lionel Moisan Abstract. In the Rudin–Osher–Fatemi (ROF) image denoising model, total variation (TV) is used as a global regularization term. However, as we observe, the local interactions induced by TV do not propagate much at long distances in practice, so that the ROF model is not far from being a local filter. In this paper, we propose building a purely local filter by considering the ROF model in a given neighborhood of each pixel. We show that appropriate weights are required to avoid aliasing- like effects, and we provide an explicit convergence criterion for an associated dual minimization algorithm based on Chambolle’s work. We study theoretical properties of the obtained local filter and show that this localization of the ROF model brings an interesting optimization of the bias-variance trade-off, and a strong reduction of an ROF drawback called the “staircasing effect.” Finally, we present a new denoising algorithm, TV-means, that efficiently combines the idea of local TV-filtering with the nonlocal means patch-based method. Key words. total variation, variational model, local filter, image denoising, nonlocal means AMS subject classifications. 68U10, 65K10, 62H35 DOI. 10.1137/100785855 1. Introduction. Image denoising/smoothing is one of the most frequently considered issues in image processing, not only because it plays a key preliminary role in many computer vision systems, but also because it is probably the simplest way to address the fundamental issue of image modeling, as a starting point towards more complex tasks such as deblurring, demosaicking, and inpainting.