A Weberized Total Variance Regularization-based Image Multiplicative Noise Model

Authors

  • Xinyao Yu University of Science and Technology Beijing
  • Donghong Zhao University of Science and Technology Beijing

DOI:

https://doi.org/10.5566/ias.2837

Keywords:

ADMM, Euler-Lagrange equation, image denoising, multiplicative noise, partial differential equation, Weberized total variation

Abstract

This paper considers Weber's law and proposes a new non-convex model for images contaminated by Gaussian noise and Rayleigh noise. The alternating direction method of multipliers (abbreviated as ADMM) is a recent popular method that can handle convex and non-convex problems well. This paper compares denoising effect between ADMM and the Euler-Lagrange equation method applied to the non-convex model. The numerical experimental results show that ADMM performs better and has a higher Peak Signal to Noise Ratio.

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Published

2023-07-10

How to Cite

Yu, X., & Zhao, D. (2023). A Weberized Total Variance Regularization-based Image Multiplicative Noise Model. Image Analysis and Stereology, 42(2), 65–76. https://doi.org/10.5566/ias.2837

Issue

Vol. 42 No. 2 (2023)

Section

Original Research Paper

License

Copyright (c) 2023 Xinyao Yu, Donghong Zhao

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.