Journal of the Korean Mathematical Society (대한수학회지)

Korean Mathematical Society (대한수학회)
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ITERATIVE REWEIGHTED ALGORITHM FOR NON-CONVEX POISSONIAN IMAGE RESTORATION MODEL

  • Jeong, Taeuk (Department of Computational Science and Engineering Yonsei University) ;
  • Jung, Yoon Mo (Department of Mathematics Sungkyunkwan University) ;
  • Yun, Sangwoon (Department of Mathematics Education Sungkyunkwan University)
  • Received : 2017.06.28
  • Accepted : 2017.09.06
  • Published : 2018.05.01
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Abstract

An image restoration problem with Poisson noise arises in many applications of medical imaging, astronomy, and microscopy. To overcome ill-posedness, Total Variation (TV) model is commonly used owing to edge preserving property. Since staircase artifacts are observed in restored smooth regions, higher-order TV regularization is introduced. However, sharpness of edges in the image is also attenuated. To compromise benefits of TV and higher-order TV, the weighted sum of the non-convex TV and non-convex higher order TV is used as a regularizer in the proposed variational model. The proposed model is non-convex and non-smooth, and so it is very challenging to solve the model. We propose an iterative reweighted algorithm with the proximal linearized alternating direction method of multipliers to solve the proposed model and study convergence properties of the algorithm.

Keywords

Acknowledgement

Supported by : National Research Foundation of Korea (NRF)

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