Presentation on theme: "Various Regularization Methods in Computer Vision Min-Gyu Park Computer Vision Lab. School of Information and Communications GIST."— Presentation transcript:
Various Regularization Methods in Computer Vision Min-Gyu Park Computer Vision Lab. School of Information and Communications GIST
Vision Problems (intro) Such as stereo matching, optical flow estimation, de- noising, segmentation, are typically ill-posed problems. –Because these are inverse problems. Properties of well-posed problems. –Existence: a solution exists. –Uniqueness: the solution is unique. –Stability: the solution continuously depends on the input data.
Vision Problems (intro) Vision problems are difficult to compute the solution directly. –Then, how to find a meaningful solution to such a hard problem? Impose the prior knowledge to the solution. –Which means we constrict the space of possible solutions to physically meaningful ones.
Vision Problems (intro) This seminar is about imposing our prior knowledge to the solution or to the scene. There are various kinds of approaches, –Quadratic regularization, –Total variation, –Piecewise smooth models, –Stochastic approaches, –With either L1 or L2 data fidelity terms. We will study about the properties of different priors.
Bayesian Inference & Probabilistic Modeling We will see the simple de-noising problem. –f is a noisy input image, u is the noise-free (de-noised) image, and n is Gaussian noise. Our objective is finding the posterior distribution, –Where the posterior distribution can be directly estimated or can be estimated as,
Bayesian Inference & Probabilistic Modeling Probabilistic modeling Depending on how we model p(u), the solution will be significantly different. Likelihood term (data fidelity term) Prior term Evidence (does not depend on u)
De-noising Problem Critical issue. –How to smooth the input image while preserving some important features such as image edge. Input (noisy) image De-noised image via L1 regularization term
De-noising Problem Formulation. Quadratic smoothness of a first order derivatives. First order: flat surface Second order: quadratic surface
De-noising Problem By combining both likelihood and prior terms, Thus, maximization of p(f|u)p(u) is equivalent to minimize the free energy of Gibbs distribution. Is the exactly Gibbs function!!!
How to minimize the energy function? Directly solve the Euler-Lagrange equations. –Because the solution space is convex! (having a globally unique solution)
The Result of a Quadratic Regularizer Input (noisy) image Noise are removed (smoothed), but edges are also blurred. The result is not satisfactory….
Why? Due to bias against discontinuities. 543210543210 1 2 3 4 5 6 Discontinuity are penalized more!!! intensity whereas L1 norm(total variation) treats both as same.
Pros & Cons If there is no discontinuity in the result such as depth map, surface, and noise-free image, quadratic regularizer will be a good solution. –L2 regulaizer is biased against discontinuities. –Easy to solve! Descent gradient will find the solution. Quadratic problems has a unique global solution. –Meaning it is a well-posed problem. –But, we cannot guarantee the solution is truly correct.
Introduction to Total Variation If we use L1-norm for the smoothness prior, Furthermore, if we assume the variance is 1 then,
Introduction to Total Variation Then, the free energy is defined as total variation of a function u. x u(x) 0 Definition of total variation:
Characteristics of Total Variation Advantages: –No bias against discontinuities. –Contrast invariant without explicitly modeling the light condition. –Robust under impulse noise. Disadvantages: –Objective functions are non-convex. Lie between convex and non-convex problems.
How to solve it? With L1, L2 data terms, we can use –Variational methods Explicit Time Marching Linearization of Euler-Lagrangian Nonlinear Primal-dual method Nonlinear multi-grid method –Graph cuts –Convex optimization (first order scheme) –Second order cone programming To solve original non-convex problems.
Variational Methods Definition. –Informally speaking, they are based on solving Euler- Lagrange equations. Problem Definition (constrained problem). The first total variation based approach in computer vision, named after Rudin, Osher and Fatemi, shortly as ROF model (1992).
Variational Methods Unconstrained (Lagrangian) model Can be solved by explicit time matching scheme as,
Variational Methods What happens if we change the data fidelity term to L1 norm as, More difficult to solve (non-convex), but robust against outliers such as occlusion. This formulation is called as TV-L1 framework.
Variational Methods Comparison among variational methods in terms of explicit time marching scheme. Where the degeneracy comes from. L2-L2 TV-L2 TV-L1
Duality-based Approach Why do we use duality instead of the primal problem? –The function becomes continuously differentiable. –Not always, but in case of total variation. For example, we use below property to introduce a dual variable p,
Duality-based Approach Deeper understandings of duality in variational methods will be given in the next seminar.
Applying to Other Problems Optical flow (Horn and Schunck – L2-L2) Stereo matching (TV-L1) Segmentation (TV-L2)