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An infrared dim and small target detection method based on fractional differential

  • Peng Li*
  • , Bin Yan
  • , Run Ye
  • , Guanghui Sun
  • *Corresponding author for this work
  • University of Electronic Science and Technology of China

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

Abstract

Dim target detection in infrared image with complex background and low signal-ratio(SCR) is a significant and difficult task in the infrared target tracking system, this paper proposes a real time dim target detection algorithm based on fractional differential algorithm. An improved top-hat operator is adopted to process the original image which can restrain the background well. After we enhance the target based on a fractional differential algorithm, to further eliminate residual clutter and gain the saliency map, we process the enhanced image with a spectral residual method. The pipeline filter can reduce the false alarm rate further at last. The algorithm is tested on three different infrared image sequences, and the experimental results show that the algorithm is efficient and robust, which would be very useful for infrared dim small target detection. In particular, the proposed method can improve the SNR of the image significantly.

Original languageEnglish
Title of host publicationProceedings of the 30th Chinese Control and Decision Conference, CCDC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2381-2386
Number of pages6
ISBN (Electronic)9781538612439
DOIs
StatePublished - 6 Jul 2018
Event30th Chinese Control and Decision Conference, CCDC 2018 - Shenyang, China
Duration: 9 Jun 201811 Jun 2018

Publication series

NameProceedings of the 30th Chinese Control and Decision Conference, CCDC 2018

Conference

Conference30th Chinese Control and Decision Conference, CCDC 2018
Country/TerritoryChina
CityShenyang
Period9/06/1811/06/18

Keywords

  • dim target
  • fractional differential
  • improved top-hat
  • pipeline filtering
  • saliency detection

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