Cartoon and Texture Decomposition-Based Color Transfer for Fabric Images

Yu Han, Chen Xu, George Baciu, Min Li, Md Robiul Islam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

42 Citations (Scopus)


A color design process for fabric images can resort to a solution of a color transfer problem based on given color themes. Usually, the color transfer process contains an image segmentation phase and an image construction phase. In this paper, a novel color transfer method for fabric images is proposed. Compared with classical color transfer methods, the new method has the following three main innovations. First, the new method, in its image segmentation phase, follows an assumption that a fabric image can be decomposed into cartoon and texture components, which means the new color transfer method, in its image segmentation, phase incorporates an image decomposition process. The advantage of the innovation is that the cartoon component is more suitable than the original image to be used to partition the fabric image. Second, the new color transfer method can generate more vivid color transfer results since the above texture component is used to describe yarn texture details in the image construction phase. Third, the total generalized variation (TGV) regularizer is used to further improve the performance of image decomposition. Here, the TGV regularizer is good at estimating the weak lightness variation of the cartoon component with the CIELab color scheme. In addition, by using the augmented Lagrange multiplier method, we derive an efficient algorithm to search for the solutions to the proposed color transfer problem. Numerical results demonstrate that the proposed color transfer method can generate better results for fabric images.
Original languageEnglish
Pages (from-to)80-92
Number of pages13
JournalIEEE Transactions on Multimedia
Issue number1
Publication statusPublished - 1 Jan 2017


  • Color transfer
  • fabric image
  • image decomposition
  • total generalized variation (TGV)
  • variational model

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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