Image descreening by GA-CNN-based texture classification

Yu-Wen Shou, Chin-Teng Lin

Research output: Contribution to journalArticlepeer-review

Abstract

This paper proposes a new image-descreening technique based on texture classification using a cellular neural network (CNN) with template trained by genetic algorithm (GA), called GA-CNN. Instead of using the fixed filters for image descreening, we are equipped with a more pliable mechanism for classifications in screening patterns. Using CNN makes it possible to get an accurate texture classification result in a faster speed by its superiority of implementable hardware and the flexible choices of templates. The use of the GA here helps us to look for the most appropriate template for CNNs more adaptively and methodically. The evolved parameters in the template for CNNs can not only provide a quicker classification mechanism but also help us with a better texture classification for screening patterns. After the class of screening patterns in the querying images is determined by the trained GA-CNN-based texture classification. system, the recommendatory filters are induced to solve the screening problems. The induction of the classification in screening patterns has simplified the choice of filters and made it valueless to determine a new structured filter. Eventually, our comprehensive methodology is going to be topped off with more desirable results and the indication for the decrease in time complexity. Index Terms-Cellular neural network (CNN), genetic algorithm (GA), image descreening, texture classification.
Original languageEnglish
Pages (from-to)2287-2299
Number of pages13
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Volume51
Issue number11
DOIs
StatePublished - Nov 2004

Keywords

  • texture classification
  • image descreening
  • genetic algorithm (GA)
  • cellular neural network (CNN)

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