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A Nonlinear PrimalDual Method For Total VariationBased Image Restoration
, 1995
"... . We present a new method for solving total variation (TV) minimization problems in image restoration. The main idea is to remove some of the singularity caused by the nondifferentiability of the quantity jruj in the definition of the TVnorm before we apply a linearization technique such as Newton ..."
Abstract

Cited by 162 (22 self)
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. We present a new method for solving total variation (TV) minimization problems in image restoration. The main idea is to remove some of the singularity caused by the nondifferentiability of the quantity jruj in the definition of the TVnorm before we apply a linearization technique such as Newton's method. This is accomplished by introducing an additional variable for the flux quantity appearing in the gradient of the objective function. Our method can be viewed as a primaldual method as proposed by Conn and Overton [8] and Andersen [3] for the minimization of a sum of Euclidean norms. Experimental results show that the new method has much improved global convergence behaviour than the primal Newton's method. 1. Introduction. During some phases of the manipulation of an image some random noise and blurring is usually introduced. The presence of this noise and blurring makes difficult and inaccurate the latter phases of the image processing. The algorithms for noise removal and debl...
A Computational Study of the Homogeneous Algorithm for LargeScale Convex Optimization
, 1997
"... Recently the authors have proposed a homogeneous and selfdual algorithm for solving the monotone complementarity problem (MCP) [5]. The algorithm is a single phase interiorpoint type method, nevertheless it yields either an approximate optimal solution or detects a possible infeasibility of th ..."
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Cited by 13 (1 self)
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Recently the authors have proposed a homogeneous and selfdual algorithm for solving the monotone complementarity problem (MCP) [5]. The algorithm is a single phase interiorpoint type method, nevertheless it yields either an approximate optimal solution or detects a possible infeasibility of the problem. In this paper we specialize the algorithm to the solution of general smooth convex optimization problems that also possess nonlinear inequality constraints and free variables. We discuss an implementation of the algorithm for largescale sparse convex optimization. Moreover, we present computational results for solving quadratically constrained quadratic programming and geometric programming problems, where some of the problems contain more than 100,000 constraints and variables. The results indicate that the proposed algorithm is also practically efficient. Department of Management, Odense University, Campusvej 55, DK5230 Odense M, Denmark. Email: eda@busieco.ou.dk y ...
Iterative Methods for Total Variation Image Restoration
, 1995
"... this paper. Others may involve nonlinear blurring operators, multiplicative noise, noise with more complicated distributions and with possible correlation with the image. The aim of image restoration is the estimation of the ideal true image from the recorded one. The direct problem of computing the ..."
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Cited by 10 (4 self)
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this paper. Others may involve nonlinear blurring operators, multiplicative noise, noise with more complicated distributions and with possible correlation with the image. The aim of image restoration is the estimation of the ideal true image from the recorded one. The direct problem of computing the imaging system response (blurred image) from a given image is often assumed to be known and wellposed.