Motion adaptive deinterlacing with modular neural networks

Hyunsoo Choi, Chul Hee Lee

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

In this letter, a motion adaptive deinterlacing algorithm based on modular neural networks is proposed. The proposed method uses different neural networks based on the amount of motion. Modular neural networks were selectively used depending on the differences between the adjacent fields. We also used motion vectors to select optimal input pixels from the adjacent fields. Motion estimation was used to find input blocks for the neural networks with minimum errors. Intra/inter-mode switching was employed to address inaccurate motion estimation problems.

Original languageEnglish
Article number5733392
Pages (from-to)844-849
Number of pages6
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume21
Issue number6
DOIs
Publication statusPublished - 2011 Jun 1

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Neural networks
Motion estimation
Adaptive algorithms
Pixels

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Media Technology

Cite this

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Motion adaptive deinterlacing with modular neural networks. / Choi, Hyunsoo; Lee, Chul Hee.

In: IEEE Transactions on Circuits and Systems for Video Technology, Vol. 21, No. 6, 5733392, 01.06.2011, p. 844-849.

Research output: Contribution to journalArticle

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