Color Image Restoration by Saturation-Value Total Variation

Zhigang Jia, Michael K. Ng, Wei Wang*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

54 Citations (Scopus)
12 Downloads (Pure)


Color image restoration is one of the important tasks in color image processing. Total variation regularizaton was proposed and employed for the recovery of edges in a grayscale image. In the literature, there are several methods for extension of total variation regularization for color images, for example, based on color channel coupling and tensor regularization. The main contribution of this paper is to propose and develop a new saturation-value (SV) color total variation regularization in the hue, saturation, amd value color space instead of in the original red, green, and blue color space. The development of this SV total variation can be studied via the representation of color images in the quaternion framework for color edge detection. We will investigate the properties of the SV total variation regularization and the resulting optimization model for color image restoration. Numerical examples are presented to demonstrate that the performance of the new SV total variation is better than that of existing color image total variation methods in terms of some criteria such as PSNR, SSIM, and S-CIELAB error.

Original languageEnglish
Pages (from-to)972-1000
Number of pages29
JournalSIAM Journal on Imaging Sciences
Issue number2
Publication statusPublished - 4 Jun 2019

Scopus Subject Areas

  • Mathematics(all)
  • Applied Mathematics

User-Defined Keywords

  • Color images
  • Color space
  • Image restoration
  • Quaternion
  • Regularization
  • Total variation


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