Communications on Applied Mathematics and Computation ›› 2024, Vol. 6 ›› Issue (2): 1406-1427.doi: 10.1007/s42967-023-00344-z

• ORIGINAL PAPERS • 上一篇    下一篇

A Second-Order Image Denoising Model for Contrast Preservation

Wei Zhu   

  1. Department of Mathematics, University of Alabama, Box 870350, Tuscaloosa, AL 35487, USA
  • 收稿日期:2022-09-21 修回日期:2023-10-21 接受日期:2023-10-24 出版日期:2024-03-15 发布日期:2024-03-15
  • 通讯作者: Wei Zhu,E-mail:wzhu7@ua.edu E-mail:wzhu7@ua.edu

A Second-Order Image Denoising Model for Contrast Preservation

Wei Zhu   

  1. Department of Mathematics, University of Alabama, Box 870350, Tuscaloosa, AL 35487, USA
  • Received:2022-09-21 Revised:2023-10-21 Accepted:2023-10-24 Online:2024-03-15 Published:2024-03-15
  • Contact: Wei Zhu,E-mail:wzhu7@ua.edu E-mail:wzhu7@ua.edu

摘要: In this work, we propose a second-order model for image denoising by employing a novel potential function recently developed in Zhu (J Sci Comput 88: 46, 2021) for the design of a regularization term. Due to this new second-order derivative based regularizer, the model is able to alleviate the staircase effect and preserve image contrast. The augmented Lagrangian method (ALM) is utilized to minimize the associated functional and convergence analysis is established for the proposed algorithm. Numerical experiments are presented to demonstrate the features of the proposed model.

关键词: Image denoising, Variational model, Image contrast, Augmented Lagrangian method (ALM)

Abstract: In this work, we propose a second-order model for image denoising by employing a novel potential function recently developed in Zhu (J Sci Comput 88: 46, 2021) for the design of a regularization term. Due to this new second-order derivative based regularizer, the model is able to alleviate the staircase effect and preserve image contrast. The augmented Lagrangian method (ALM) is utilized to minimize the associated functional and convergence analysis is established for the proposed algorithm. Numerical experiments are presented to demonstrate the features of the proposed model.

Key words: Image denoising, Variational model, Image contrast, Augmented Lagrangian method (ALM)