Adaptive Proximal Point Algorithms for Total Variation Image Restoration

  • Ying Chen School of Mathematics and Computer Sciences, Gannan Normal University, China
  • Jian Wu College of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Guangdong province, China
  • Gaohang Yu School of Mathematics and Computer Sciences, Gannan Normal University, China


Image restoration is a fundamental problem in various areas of imaging sciences. This paper presents a class of adaptive proximal point algorithms (APPA) with contraction strategy for total variational image restoration. In each iteration, the proposed methods choose an adaptive proximal parameter matrix which is not necessary symmetric. In fact, there is an inner extrapolation in the prediction step, which is followed by a correction step for contraction. And the inner extrapolation is implemented by an adaptive scheme. By using the framework of contraction method, global convergence result and a convergence rate of O(1/N) could be established for the proposed methods. Numerical results are reported to illustrate the efficiency of the APPA methods for solving total variation image restoration problems. Comparisons with the state-of-the-art algorithms demonstrate that the proposed methods are comparable and promising.


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How to Cite
Chen, Y., Wu, J., & Yu, G. (2015). Adaptive Proximal Point Algorithms for Total Variation Image Restoration. Statistics, Optimization & Information Computing, 3(1), 15-29.
Research Articles