Journal of Scientific Computing

, Volume 75, Issue 2, pp 713–732 | Cite as

A Semi-smooth Newton Method for Inverse Problem with Uniform Noise

  • You-Wei Wen
  • Wai-Ki Ching
  • Michael Ng


In this paper we study inverse problems where observations are corrupted by uniform noise. By using maximum a posteriori approach, an \(L_\infty \)-norm constrained minimization problem can be formulated for uniform noise removal. The main difficulty of solving such minimization problem is how to deal with non-differentiability of the \(L_\infty \)-norm constraint and how to estimate the level of uniform noise. The main contribution of this paper is to develop an efficient semi-smooth Newton method for solving this minimization problem. Here the \(L_\infty \)-norm constraint can be handled by active set constraints arising from the optimality conditions. In the proposed method, linear systems based on active set constraints are required to solve in each Newton step. We also employ the method of moments (MoM) to estimate the level of uniform noise for the minimization problem. The combination of the proposed method and MoM is quite effective for solving inverse problems with uniform noise. Numerical examples are given to demonstrate that our proposed method outperforms the other testing methods.


Inverse problem Uniform noise Semi-smooth Newton method \(L_\infty \)-norm constraint Linear systems 



The authors would like to thank the two anonymous referees and the editor for their helpful comments and suggestions.


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© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Key Laboratory of High Performance Computing and Stochastic Information Processing (HPCSIP), College of Mathematics and Computer ScienceHunan Normal UniversityChangshaPeople’s Republic of China
  2. 2.Advanced Modeling and Applied Computing Laboratory, Department of MathematicsThe University of Hong KongHong KongPeople’s Republic of China
  3. 3.Department of Mathematics Hong KongBaptist UniversityHong KongPeople’s Republic of China

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