In this paper, we study the minimizing total variation flow u(t) = div(Du/DU) in R-N for initial data u(0) in L-loc(1)(R-N), proving an existence and uniqueness result. Then we characterize all bounded sets Omega of finite perimeter in R-2 which evolve without distortion of the boundary. In that case, no = chi(Omega) evolves as u(t, x) = (1 - lambda(Omega)t)(+) chi(Omega),, where chi(Omega) is the characteristic function of Omega, lambda(Omega) := P(Omega)/Omega, and P(Omega) denotes the perimeter of Omega. We give examples of such sets. The solutions are such that upsilon := lambda(Omega)chi(Omega) solves the eigenvalue problem -div(Dupsilon/Dupsilon) = upsilon. We construct other explicit solutions of this problem. As an application, we construct explicit solutions of the denoising problem in image processing. (C) 2002 Elsevier Science (USA).
Bellettini, G., Caselles, V., Novaga, M. (2002). The total variation flow in Rn. JOURNAL OF DIFFERENTIAL EQUATIONS, 184(2), 475-525 [10.1006/jdeq.2001.4150].
The total variation flow in Rn
BELLETTINI, GIOVANNI;
2002-01-01
Abstract
In this paper, we study the minimizing total variation flow u(t) = div(Du/DU) in R-N for initial data u(0) in L-loc(1)(R-N), proving an existence and uniqueness result. Then we characterize all bounded sets Omega of finite perimeter in R-2 which evolve without distortion of the boundary. In that case, no = chi(Omega) evolves as u(t, x) = (1 - lambda(Omega)t)(+) chi(Omega),, where chi(Omega) is the characteristic function of Omega, lambda(Omega) := P(Omega)/Omega, and P(Omega) denotes the perimeter of Omega. We give examples of such sets. The solutions are such that upsilon := lambda(Omega)chi(Omega) solves the eigenvalue problem -div(Dupsilon/Dupsilon) = upsilon. We construct other explicit solutions of this problem. As an application, we construct explicit solutions of the denoising problem in image processing. (C) 2002 Elsevier Science (USA).File | Dimensione | Formato | |
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https://hdl.handle.net/11365/1017473