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Cloud Based Image Contrast Enhancement

  • Shiqi Wang
  • , Ke Gu
  • , Siwei Ma
  • , Weisi Lin
  • , Xiang Zhang
  • , Wen Gao
  • Peking University
  • Shanghai Jiao Tong University
  • Nanyang Technological University

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

Abstract

We propose a cloud based image contrast enhancement framework, in which the context-sensitive and context-free contrast is improved via solving a multi-criteria optimization problem. Specifically, the context-sensitive contrast enhancement is based on the unsharp masking of the input and edge-preserving filtered images, while the context-free contrast enhancement is achieved by the sigmoid transfer mapping. The parameters in the optimization process are determined with the reference to the image that has a similar content and better enhancement quality in the cloud. The image complexity from the free energy based brain theory and the 'surface' quality statistics is collaboratively optimized to infer the parameters. Experimental results demonstrate that the proposed technique can efficiently create visually-pleasing enhanced images with the guidance image from cloud.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages148-155
Number of pages8
ISBN (Electronic)9781479986880
DOIs
StatePublished - 9 Jul 2015
Externally publishedYes
Event1st IEEE International Conference on Multimedia Big Data, BigMM 2015 - Beijing, China
Duration: 20 Apr 201522 Apr 2015

Publication series

NameProceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015

Conference

Conference1st IEEE International Conference on Multimedia Big Data, BigMM 2015
Country/TerritoryChina
CityBeijing
Period20/04/1522/04/15

Keywords

  • cloud image
  • Contrast enhancement
  • sigmoid transfer mapping
  • unsharp masking

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