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Automatic Image Resampling Filter Generation

Author

Listed:
  • Costin-Anton Boiangiu

    (Politehnica University of Bucharest, Bucharest, Romania)

  • Ionut-Adrian Nitu

    (Politehnica University of Bucharest, Bucharest, Romania)

Abstract

The digital world, that we’re in constant contact with, is filled with images of all sorts and shapes. However, the resolution of some of those is just not good enough in several scenarios: feature recognition, OCR, document processing, machine vision, and so on, thus making the resampling of the images a very important step towards a correct processing, For the purpose of this work, several existing filters have been analyzed using a comprehensive set of test images. The main goal is to create new filters by using different approaches: genetic algorithms - by generating populations of filters and keeping the best individuals based on a mean error criteria, brute-force solution searching– by selecting the most efficient filters.

Suggested Citation

  • Costin-Anton Boiangiu & Ionut-Adrian Nitu, 2016. "Automatic Image Resampling Filter Generation," Romanian Economic Business Review, Romanian-American University, vol. 10(2), pages 487-502, December.
    • Handle: RePEc:rau:journl:v:10:y:2016:i:2:p:487-502
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    File URL: http://www.rebe.rau.ro/RePEc/rau/jisomg/WI16/JISOM-WI16-A20.pdf
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    References listed on IDEAS

    as
    1. Florin Manaila & Costin-Anton Boiangiu & Ion Bucur, 2014. "Super Resolution From Multiple Low Resolution Images," Romanian Economic Business Review, Romanian-American University, vol. 8(2), pages 316-322, December.
    2. Alexandra Ghecenco, 2014. "Principles Of Image Deblurring," Romanian Economic Business Review, Romanian-American University, vol. 8(2), pages 488-497, December.
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