Restoration of half-toned color-quantized images using particle swarm optimization with multi-wavelet mutation

Hung Fat Frank Leung, Benny C.W. Yeung, Y. H. Chan

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review

Abstract

Restoration of color-quantized images is rarely addressed in the literature, especially when the images are color-quantized with half-toning. Many existing restoration algorithms are inadequate to deal with this problem because they were proposed for restoring noisy blurred images only. In this chapter, a restoration algorithm based on Particle Swarm Optimization with multi-wavelet mutation (MWPSO) is proposed to solve the problem. This algorithm makes good use of the available color palette and the mechanism of a half-toning process to derive useful a priori information for the restoration. Simulation results show that it can improve the quality of a half-toned color-quantized image remarkably in terms of both signal-to-noise ratio improvement and convergence rate. The subjective quality of the restored images can also be improved.
Original languageEnglish
Title of host publicationComputational Intelligence and its Applications
Subtitle of host publicationEvolutionary Computation, Fuzzy Logic, Neural Network and Support Vector Machine Techniques
PublisherImperial College Press
Pages39-57
Number of pages19
ISBN (Electronic)9781848166929
ISBN (Print)9781848166912
DOIs
Publication statusPublished - 1 Jan 2012

ASJC Scopus subject areas

  • Computer Science(all)

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