We propose an algorithm for minimizing the total variation of an image, and provide a proof of convergence. We show applications to image denoising, zooming, and the computation of the mean curvature motion of interfaces.

A constrained optimization type of numerical algorithm for removing noise from images is presented. The total variation of the image is minimized subject to constraints involving the statistics of the noise. The constraints are imposed using Lagrange multipliers. The solution is obtained using

Total Variation (TV) methods are very effective for recovering "blocky", possibly discontinuous, images from noisy data. A fixed point algorithm for minimizing a TV-penalized least squares functional is presented and compared with existing minimization schemes. A variant of the cell

In this paper, we present a blind deconvolution algorithm based on the total variational (TV) minimization method proposed in [11]. The motivation for regularizing with the TV norm is that it is extremely effective for recovering edges of images [11] as well as some blurring functions, e.g. motion

sensed video via interframe total variation minimization

method for the selection of

A CT reconstruction approach from sparse

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. DOI: 10.1109/ICDAR.2009.210 Pre-processing of degraded printed documents by Non-local Means

(Submitted by: Jerry Goldstein) Abstract. We prove existence and uniqueness of weak solutions for the minimizing total variation flow with initial data in L 1. We prove that the length of the level sets of the solution, i.e., the boundaries of the level sets, decreases with time, as one would

In this paper we further explore a class of high order TVD �total variation diminishing� Runge-Kutta time discretization initialized in �12�, suitable for solving hyperbolic conser-vation laws with stable spatial discretizations. We illustrate with numerical examples that non-TVD but linearly