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ENAS-RIF algorithm for image restoration

  • Yang Yang
  • , Zhen Wen Yang
  • , Tian Shuang Shen
  • , Bo Chen*
  • *Corresponding author for this work
  • Information Engineering University
  • Air Force Airborne Academy

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

Abstract

Image of objects is inevitably encountered by space-based working in the atmospheric turbulence environment, such as those used in astronomy, remote sensing and so on. The observed images are seriously blurred. The restoration is required for reconstruction turbulence degraded images. In order to enhance the performance of image restoration, a novel enhanced nonnegativity and support constants recursive inverse filtering(ENAS-RIF) algorithm was presented, which was based on the reliable support region and enhanced cost function. Firstly, the Curvelet denoising algorithm was used to weaken image noise. Secondly, the reliable object support region estimation was used to accelerate the algorithm convergence. Then, the average gray was set as the gray of image background pixel. Finally, an object construction limit and the logarithm function were add to enhance algorithm stability. The experimental results prove that the convergence speed of the novel ENAS-RIF algorithm is faster than that of NAS-RIF algorithm and it is better in image restoration.

Original languageEnglish
Title of host publicationOptoelectronic Imaging and Multimedia Technology II
DOIs
StatePublished - 2012
Externally publishedYes
EventOptoelectronic Imaging and Multimedia Technology II - Beijing, China
Duration: 5 Nov 20127 Nov 2012

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8558
ISSN (Print)0277-786X

Conference

ConferenceOptoelectronic Imaging and Multimedia Technology II
Country/TerritoryChina
CityBeijing
Period5/11/127/11/12

Keywords

  • cost function
  • curvelet transform
  • image restoration
  • reliable support region

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