Efficient implementation of neural network deinterlacing

Guiwon Seo, Hyunsoo Choi, Chulhee Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


Interlaced scanning has been widely used in most broadcasting systems. However, there are some undesirable artifacts such as jagged patterns, flickering, and line twitters. Moreover, most recent TV monitors utilize flat panel display technologies such as LCD or PDP monitors and these monitors require progressive formats. Consequently, the conversion of interlaced video into progressive video is required in many applications and a number of deinterlacing methods have been proposed. Recently deinterlacing methods based on neural network have been proposed with good results. On the other hand, with high resolution video contents such as HDTV, the amount of video data to be processed is very large. As a result, the processing time and hardware complexity become an important issue. In this paper, we propose an efficient implementation of neural network deinterlacing using polynomial approximation of the sigmoid function. Experimental results show that these approximations provide equivalent performance with a considerable reduction of complexity. This implementation of neural network deinterlacing can be efficiently incorporated in HW implementation.

Original languageEnglish
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Image Processing
Subtitle of host publicationAlgorithms and Systems VII
Publication statusPublished - 2009
EventImage Processing: Algorithms and Systems VII - San Jose, CA, United States
Duration: 2009 Jan 192009 Jan 22

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherImage Processing: Algorithms and Systems VII
Country/TerritoryUnited States
CitySan Jose, CA

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


Dive into the research topics of 'Efficient implementation of neural network deinterlacing'. Together they form a unique fingerprint.

Cite this