Multiplicative Noise Removal Based on Unbiased Box-Cox Transformation

Yu Mei Huang, Hui Yin Yan, Tieyong ZENG*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Multiplicative noise removal is a challenging problem in image restoration. In this paper, by applying Box-Cox transformation, we convert the multiplicative noise removal problem into the additive noise removal problem and the block matching three dimensional (BM3D) method is applied to get the final recovered image. Indeed, BM3D is an effective method to remove additive Gaussian white noise in images. A maximum likelihood method is designed to determine the parameter in the Box-Cox transformation. We also present the unbiased inverse transform for the Box-Cox transformation which is important. Both theoretical analysis and experimental results illustrate clearly that the proposed method can remove multiplicative noise very well especially when multiplicative noise is heavy. The proposed method is superior to the existing methods for multiplicative noise removal in the literature.

Original languageEnglish
Pages (from-to)803-828
Number of pages26
JournalCommunications in Computational Physics
Volume22
Issue number3
DOIs
Publication statusPublished - 1 Sep 2017

Scopus Subject Areas

  • Physics and Astronomy (miscellaneous)

User-Defined Keywords

  • block-matching three dimensional method (BM3D)
  • Box-Cox transformation
  • maximum likelihood estimation
  • Multiplicative noise removal

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