A novel adaptive SVR based filter ASBF for image restoration

Jiagang Zhu, Shitong Wang, Xisheng Wu, Fu Lai Korris Chung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)


In this paper, a novel adaptive filter ASBF based on support vector regression (SVR) is proposed to preserve more image details and efficiently suppress impulse noise simultaneously. The main idea of the novel filter ASBF here is to employ a SVR based impulse detector to judge whether an input pixel is contaminated or not by impulse noise. If this case happens, a median filter is employed to remove the corresponding impulse noise. This judg ment procedure is executed by regressing the filter window of an input pixel using SVR and then judging the input pixel by its regression distance. Huber loss function is used in SVR regression, due to its excellent robustness capability. The distinctive advantage of the filter ASBF over the latest Support Vector Classifier (SVC) based filter is that no training for the original noise-free image is required in our approach, which is well in accordance with our visual judgment way. Experimental results for benchmark images demonstrate that our filter ASBF here outperforms the extensively-used median-based filters and the SVC based filter.
Original languageEnglish
Pages (from-to)665-672
Number of pages8
JournalSoft Computing
Issue number8
Publication statusPublished - 1 Jun 2006


  • Adaptive filter
  • Image restoration
  • Median filter
  • Support vector regression

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Geometry and Topology


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