Fuzzy rough sets hybrid scheme for motion and scene complexity adaptive deinterlacing

Gwanggil Jeon, Marco Anisetti, Donghyung Kim, Valerio Bellandi, Ernesto Damiani, Jechang Jeong

Research output: Contribution to journalArticlepeer-review

46 Scopus citations


Current research activities in the field of deinterlacing include the selection of suitable deinterlacing methods and the estimation of the exact value of a missing line. This paper proposes a spatio-temporal domain fuzzy rough sets rule for selecting a deinterlacing method that is suitable for regions with high motion or frequent scene changes. The proposed algorithm consists of two parts. The first part is fuzzy rule-based edge-direction detection with an edge preserving part that utilizes fuzzy theory to find the most accurate edge direction and interpolates the missing pixels. Using the introduced gradients in the interpolation, the vertical resolution in the deinterlaced image is subjectively concealed. The second part of the proposed algorithm is a rough sets-assisted optimization which selects the most suitable of five different deinterlacing methods and successively builds approximations of the deinterlaced sequence. Moreover, this approach employs a size reduction of the database system, keeping only the information essential for the process. The proposed algorithm is intended not only to be fast, but also to reduce deinterlacing artifacts.

Original languageBritish English
Pages (from-to)425-436
Number of pages12
JournalImage and Vision Computing
Issue number4
StatePublished - 3 Mar 2009


  • Deinterlacing
  • Directional interpolation
  • Fuzzy control
  • Motion analysis
  • Rough set
  • Scene complexity analysis


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