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Introduction to Variational Methods and Applications Chunming Li Institute of Imaging Science Vanderbilt University URL: www.vuiis.vanderbilt.edu/~licm E-mail: [email protected] 1 Outline 1. Brief introduction to calculus of variations 2. Applications: • Total variation model for image denoising • Region-based level set methods • Multiphase level set methods 2 A Variational Method for Image Denoising Original image Denoised image by TV 3 Total Variation Model (Rudin-Osher-Fatemi) • Minimize the energy functional: where I is an image. Original image I Denoised image by TV Gaussian Convolution 4 Introduction to Calculus of Variations 5 What is Functional and its Derivative? • A functional is a mapping space of infinite dimension where the domain is a • Usually, the space is a set of functions with certain properties (e.g. continuity, smoothness). • Can we find the minimizer of a functional F(u) by solving F’(u)=0? • What is the “derivative” of a functional F(u) ? 6 Hilbert Spaces A real Hilbert Space X is endowed with the following operations: 1. Vector addition: 2. Scalar multiplication: 3. Inner product , with properties: 4. Norm Basic facts of a Hilbert Space X 1. X is complete 2. Cauchy-Schwarz inequality holds if and only if where the equality 7 Space The space • Inner product: • Norm: is a linear space. 8 Linear Functional on Hilbert Space • A linear functional on Hilbert space X is a mapping with property: for any • A functional that is bounded if there is a constant c such for all • The space of all bounded linear functionals on X is called the dual space of X, denoted by X’. • Linear functionals deduced from inner product: For a given vector , the functional is a bounded linear functional. • Theorem: Let linear functional that be a Hilbert space. Then, for any bounded , there exists a vector such for all 9 Directional Derivative of Functional • Let be a functional on Hilbert space X, we call the directional derivative of F at x in the direction v if the limit exists. • Furthermore, if is a bounded linear functional of v, we say F is Gateaux differentiable. • Since is a linear functional on Hilbert space, there exists a vector such that ,then is called the Gateaux derivative of , and we write . • If all is a minimizer of the functional , then , i.e. . (Euler-Lagrange Equation) for 10 Example • Consider the functional F(u) on space defined by: • Rewrite F(u) with inner product • For any v, compute: • It can be shown that • Solve Minimizer 11 A short cut • Rewrite as: where the equality holds if and only if Minimizer 12 An Important Class of Functionals • Consider energy functionals in the form: where is a function with variables: • Gateaux derivative: 13 Proof • Denote by the space of functions that are infinitely continuous differentiable, with compact support. • The subspace is dense in the space • Compute for any • Lemma: for any (integration by part) 14 Let 15 16 Steepest Descent • The directional derivative of F at given by • What is the direction steepest descent? in the direction of is in which the functional F has • Answer: The directional derivative and the absolute value is negative, is maximized. The direction of steepest descent 17 Gradient Flow • Gradient flow (steepest descent flow) is: • Gradient flow describes the motion of u in the space X toward a local minimum of F. • For energy functional: the gradient flow is: 18 Example: Total Variation Model • Consider total variation model: • The procedure of finding the Gateaux derivative and gradient flow: 1. Define the Lagrangian in 2. Compute the partial derivatives of 3. Compute the Gateaux derivative 19 Example: Total Variation Model with Gateaux derivative 4. Gradient Flow 20 Region Based Methods 21 Mumford-Shah Functional Regularization term Data fidelity term Smoothing term 22 Active Contours without Edges (Chan & Vese 2001) 23 Active Contours without Edges 24 Results 25 Multiphase Level Set Formulation (Vese & Chan, 2002) c1 c2 c3 c4 26 Piece Wise Constant Model 27 Piece Wise Constant Model 28 Drawback of Piece Wise Constant Model Chan-Vese LBF Click to see the movie See: http://vuiis.vanderbilt.edu/~licm/research/LBF.html 29 Piece Smooth Model 30 Piece Smooth Model 31 Rerults 32 Thank you 33