An inverse halftoning algorithm based on neural networks and UP(x) atomic function

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

Halftoning and inverse halftoning algorithms are very important image processing tools that have been widely used in digital printers, scanners, steganography and image authentication systems. Because such applications require obtaining high quality gray scale images from its halftoning versions, several inverse halftoning algorithms have been proposed during the last several years, which provide gray scale images with Peak Signal to Noise Ratio (PSNR) of about 25 to 28 dB. Although this may be enough for several applications, exist several other that require higher image quality. To this end, this paper proposes an inverse halftoning algorithm based on Upx atomic function and multilayer perceptron neural network. Experimental results show that proposed scheme provides gray scale images with PSNRs higher than 30dB independently of the method used to generate the halftone image.

Original languageEnglish
Title of host publication2015 38th International Conference on Telecommunications and Signal Processing, TSP 2015
EditorsKarol Molnar, Norbert Herencsar
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages523-527
Number of pages5
ISBN (Electronic)9781479984985
DOIs
StatePublished - 9 Oct 2015
Event2015 38th International Conference on Telecommunications and Signal Processing, TSP 2015 - Prague, Czech Republic
Duration: 9 Jul 201511 Jul 2015

Publication series

Name2015 38th International Conference on Telecommunications and Signal Processing, TSP 2015

Conference

Conference2015 38th International Conference on Telecommunications and Signal Processing, TSP 2015
Country/TerritoryCzech Republic
CityPrague
Period9/07/1511/07/15

Keywords

  • Halftoning
  • UP(x) atomic function
  • back propagation algorithm
  • inverse halftone
  • multilayer perceptron

Fingerprint

Dive into the research topics of 'An inverse halftoning algorithm based on neural networks and UP(x) atomic function'. Together they form a unique fingerprint.

Cite this